{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\k1801607\Dropbox\New_politicization\RR_PSRM_replica\session.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}29 Jun 2019, 18:37:38
{txt}
{com}. 
.  
. *we first run the necessary analysis, and then code the entries for table 1 at the end of this part
. 
. *1st step: constructing matching identifiers for basic, augmented and full model
. 
. *BASIC model
. cem  education agea (24 34 44 54 64) female bebe minority  y  election(#0), treatment(voting)  
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}10593
{txt}Number of matched strata: {res}3730

           {txt}    0      1
      All  {res}11137  44900
{txt}  Matched  {res} 9122  29826
{txt}Unmatched  {res} 2015  15074


{txt}Multivariate L1 distance: {res}.63286819

{txt}Univariate imbalance:

                 L1      mean       min       25%       50%       75%       max
education  {res} 3.0e-14  -6.1e-14         0         0         0         0         .
{txt}     agea  {res}  .06223   -.07026         1         0         0         0         .
{txt}   female  {res} 2.3e-14  -1.1e-14         0         0         0         0         0
{txt}     bebe  {res} 2.0e-14  -1.2e-14         0         0         0         0         .
{txt} minority  {res} 1.6e-15  -2.2e-15         0         0         0         0         .
{txt}        y  {res} 2.6e-14  -5.0e-14         0         0         0         0         .
{txt} election  {res} 2.9e-14  -8.1e-13         0         0         0         0         0
{txt}
{com}. *create cem_identifiers for BASIC model specification
. gen cem_basic=cem_matched
{txt}
{com}. gen cem_strata_basic=cem_strata
{txt}
{com}. 
. *AUGMENTED  model
.  cem  education agea (24 34 44 54 64) female bebe minority  y uemp3m  source(#0)  election(#0), treatment(voting)  
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}21658
{txt}Number of matched strata: {res}3924

           {txt}    0      1
      All  {res}11137  44900
{txt}  Matched  {res} 6903  18850
{txt}Unmatched  {res} 4234  26050


{txt}Multivariate L1 distance: {res}.6504337

{txt}Univariate imbalance:

                 L1      mean       min       25%       50%       75%       max
education  {res} 6.7e-15  -1.4e-14         0         0         0         0         .
{txt}     agea  {res}  .07103   -.18618         1         1         0         0         .
{txt}   female  {res} 9.3e-15  -5.3e-15         0         0         0         0         0
{txt}     bebe  {res} 1.0e-14  -1.1e-14         0         0         0         0         .
{txt} minority  {res} 2.3e-16         0         0         0         0         0         .
{txt}        y  {res} 6.7e-15  -1.8e-14         0         0         0         0         .
{txt}   uemp3m  {res} 5.1e-15  -4.4e-15         0         0         0         0         .
{txt}   source  {res} 8.0e-15  -4.2e-15         0         0         0         0         .
{txt} election  {res} 1.9e-15   7.1e-14         0         0         0         0         0
{txt}
{com}.  *create cem_identifiers for AUGMENTED model specification
. gen cem_augmented=cem_matched
{txt}
{com}. gen cem_strata_augmented=cem_strata
{txt}
{com}. 
. **FULL model
. cem  education agea (24 34 44 54 64) female bebe   minority  y uemp3m  source(#0) polintr  election(#0), treatment(voting)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}32584
{txt}Number of matched strata: {res}3167

           {txt}    0      1
      All  {res}11137  44900
{txt}  Matched  {res} 4574   9588
{txt}Unmatched  {res} 6563  35312


{txt}Multivariate L1 distance: {res}.65719808

{txt}Univariate imbalance:

                 L1      mean       min       25%       50%       75%       max
education  {res} 1.4e-14  -1.9e-14         0         0         0         0         .
{txt}     agea  {res}  .06582   -.30305         1         1         1        -1         .
{txt}   female  {res} 1.4e-14   2.7e-15         0         0         0         0         0
{txt}     bebe  {res} 1.2e-14   2.9e-15         0         0         0         0         0
{txt} minority  {res} 2.5e-16         0         0         0         0         0         .
{txt}        y  {res} 1.4e-14  -1.6e-15         0         0         0         0         .
{txt}   uemp3m  {res} 9.2e-15   1.3e-15         0         0         0         0         .
{txt}   source  {res} 1.4e-14   2.5e-14         0         0         0         0         .
{txt}  polintr  {res} 1.5e-14   4.6e-14         0         0         0         0         .
{txt} election  {res} 1.1e-14  -2.1e-13         0         0         0         0         0
{txt}
{com}. *create cem_identifiers for FULL model specification
. gen cem_full=cem_matched
{txt}
{com}. gen cem_strata_full=cem_strata
{txt}
{com}. 
. gen PScem_basic = PS if cem_basic==1
{txt}(26,211 missing values generated)

{com}. gen leftcem_basic = left if cem_basic==1
{txt}(26,211 missing values generated)

{com}. gen greencem_basic = green if cem_basic==1
{txt}(26,211 missing values generated)

{com}. 
. gen PScem_augmented = PS if cem_augmented ==1
{txt}(37,187 missing values generated)

{com}. gen leftcem_augmented = left if cem_augmented ==1
{txt}(37,187 missing values generated)

{com}. gen greencem_augmented = green if cem_augmented ==1
{txt}(37,187 missing values generated)

{com}. 
. gen PScem_full = PS if cem_full ==1
{txt}(46,449 missing values generated)

{com}. gen leftcem_full = left if cem_full ==1
{txt}(46,449 missing values generated)

{com}. gen greencem_full= green if cem_full ==1
{txt}(46,449 missing values generated)

{com}. 
. *
. gen all_left=.
{txt}(56,037 missing values generated)

{com}. replace all_left=0 if voting==1
{txt}(44,900 real changes made)

{com}. replace all_left=1 if PS==1 & voting==1
{txt}(11,261 real changes made)

{com}. replace all_left=1 if left==1 & voting==1
{txt}(2,396 real changes made)

{com}. replace all_left=1 if green==1 & voting==1
{txt}(2,310 real changes made)

{com}. 
. gen all_leftcem_full = all_left if cem_full ==1
{txt}(46,449 missing values generated)

{com}. gen incumbentcem_full = incumbent if cem_full ==1
{txt}(46,449 missing values generated)

{com}. *
. 
. *2nd step: compute imputation for Logit and logit plus, for each party  (PS, left, green) and each specification
. 
. *1st column: basic
. quietly: logit PS  education agea female bebe minority    i.election  //compute determinants of vote for PS in basic model
{txt}
{com}. predict PSlogit_basic if voting==0 
{txt}(option {bf:pr} assumed; Pr(PS))
(45,214 missing values generated)

{com}. *2nd column: augmented
. quietly: logit PS   education agea female bebe minority   y uemp3m  i.source    i.election  //compute determinants of vote for PS in augmented model
{txt}
{com}. predict PSlogit_augmented if voting==0
{txt}(option {bf:pr} assumed; Pr(PS))
(45,525 missing values generated)

{com}. *3rd column: full
. quietly: logit PS   education agea female bebe minority   y uemp3m  i.source polintr    i.election    //compute determinants of vote for PS in full model
{txt}
{com}. predict PSlogit_full if voting==0
{txt}(option {bf:pr} assumed; Pr(PS))
(45,563 missing values generated)

{com}. 
. 
. *1st column: basic
. quietly: logit PS  education agea female bebe minority      i.election if cem_basic==1 
{txt}
{com}. predict PSlogitplus_basic if voting==0  & cem_basic==1
{txt}(option {bf:pr} assumed; Pr(PS))
(46,967 missing values generated)

{com}. *2nd column: augmented
. quietly:  logit PS     education agea female bebe minority   y uemp3m  i.source     i.election  if cem_augmented==1
{txt}
{com}. predict PSlogitplus_augmented  if voting==0  & cem_augmented==1
{txt}(option {bf:pr} assumed; Pr(PS))
(49,189 missing values generated)

{com}. *3rd column: full
. quietly:  logit PS     education agea female bebe minority   y uemp3m  i.source polintr     i.election   if cem_full==1
{txt}
{com}. predict PSlogitplus_full  if voting==0  & cem_full==1
{txt}(option {bf:pr} assumed; Pr(PS))
(51,485 missing values generated)

{com}. 
. 
. *Abstainers (logit)
. 
. *1st column: basic
. quietly: logit left  education agea female bebe minority    i.election  //compute determinants of vote for PS in basic model
{txt}
{com}. predict leftlogit_basic if voting==0 
{txt}(option {bf:pr} assumed; Pr(left))
(48,526 missing values generated)

{com}. *2nd column: augmented
. quietly: logit left   education agea female bebe minority   y uemp3m  i.source    i.election  //compute determinants of vote for PS in augmented model
{txt}
{com}. predict leftlogit_augmented if voting==0
{txt}(option {bf:pr} assumed; Pr(left))
(48,749 missing values generated)

{com}. *3rd column: full
. quietly: logit left   education agea female bebe minority   y uemp3m  i.source polintr    i.election    //compute determinants of vote for PS in full model
{txt}
{com}. predict leftlogit_full if voting==0
{txt}(option {bf:pr} assumed; Pr(left))
(48,776 missing values generated)

{com}. 
. *all_left: full
. quietly: logit all_left   education agea female bebe minority   y uemp3m  i.source polintr    i.election    //compute determinants of vote for PS in full model
{txt}
{com}. predict all_leftlogit_full if voting==0
{txt}(option {bf:pr} assumed; Pr(all_left))
(45,563 missing values generated)

{com}. *incumbent: full
. quietly: logit incumbent   education agea female bebe minority   y uemp3m  i.source polintr    i.election    //compute determinants of vote for PS in full model
{txt}
{com}. predict incumbentlogit_full if voting==0
{txt}(option {bf:pr} assumed; Pr(incumbent))
(45,563 missing values generated)

{com}. 
. *Abstainers (logit +)
. 
. *1st column: basic
. quietly: logit left  education agea female bebe minority      i.election if cem_basic==1 
{txt}
{com}. predict leftlogitplus_basic if voting==0  & cem_basic==1
{txt}(option {bf:pr} assumed; Pr(left))
(49,742 missing values generated)

{com}. *2nd column: augmented
. quietly:  logit left     education agea female bebe minority   y uemp3m  i.source     i.election  if cem_augmented==1
{txt}
{com}. predict leftlogitplus_augmented  if voting==0  & cem_augmented==1
{txt}(option {bf:pr} assumed; Pr(left))
(51,217 missing values generated)

{com}. *3rd column: full
. quietly:  logit left     education agea female bebe minority   y uemp3m  i.source polintr     i.election   if cem_full==1
{txt}
{com}. predict leftlogitplus_full  if voting==0  & cem_full==1
{txt}(option {bf:pr} assumed; Pr(left))
(52,767 missing values generated)

{com}. 
. 
. **GREEN PARTIES
. 
. *Abstainers (logit)
. 
. *1st column: basic
. quietly: logit green  education agea female bebe minority    i.election  //compute determinants of vote for PS in basic model
{txt}
{com}. predict greenlogit_basic if voting==0 
{txt}(option {bf:pr} assumed; Pr(green))
(47,498 missing values generated)

{com}. *2nd column: augmented
. quietly: logit green   education agea female bebe minority   y uemp3m  i.source    i.election  //compute determinants of vote for PS in augmented model
{txt}
{com}. predict greenlogit_augmented if voting==0
{txt}(option {bf:pr} assumed; Pr(green))
(47,736 missing values generated)

{com}. *3rd column: full
. quietly: logit green   education agea female bebe minority   y uemp3m  i.source polintr    i.election    //compute determinants of vote for PS in full model
{txt}
{com}. predict greenlogit_full if voting==0
{txt}(option {bf:pr} assumed; Pr(green))
(47,769 missing values generated)

{com}. 
. *Abstainers (logit +)
. 
. *1st column: basic
. quietly: logit green  education agea female bebe minority      i.election if cem_basic==1 
{txt}
{com}. predict greenlogitplus_basic if voting==0  & cem_basic==1
{txt}(option {bf:pr} assumed; Pr(green))
(48,910 missing values generated)

{com}. *2nd column: augmented
. quietly:  logit green     education agea female bebe minority   y uemp3m  i.source     i.election  if cem_augmented==1
{txt}
{com}. predict greenlogitplus_augmented if voting==0  & cem_augmented==1
{txt}(option {bf:pr} assumed; Pr(green))
(50,605 missing values generated)

{com}. *3rd column: full
. quietly:  logit green     education agea female bebe minority   y uemp3m  i.source polintr     i.election   if cem_full==1
{txt}
{com}. predict greenlogitplus_full  if voting==0  & cem_full==1
{txt}(option {bf:pr} assumed; Pr(green))
(52,361 missing values generated)

{com}. 
. *3rd step: here we predict turnout
. 
. 
. *Compulsory (CEM)
. 
. *this is obtained by summing up : [actual voters (44900 ) + matched abstainers (voting==0  & cem_basic==1)] / (All population)
. 
. *1st column: basic
. gen abstain_match_basic = cem_basic*(11137) if voting==0
{txt}(44,900 missing values generated)

{com}. gen turnout_basic = (abstain_match_basic+44900 )/56037
{txt}(44,900 missing values generated)

{com}. *2nd column: augmented
. gen abstain_match_augmented = cem_augmented*(11137)  if voting==0
{txt}(44,900 missing values generated)

{com}. gen turnout_augmented = (abstain_match_augmented+44900 )/56037
{txt}(44,900 missing values generated)

{com}. 
. *3rd column: full
. gen abstain_match_full = cem_full*(11137)  if voting==0
{txt}(44,900 missing values generated)

{com}. gen turnout_full= (abstain_match_full+44900 )/56037
{txt}(44,900 missing values generated)

{com}. 
. 
. 
.  *Code for Figure at the end of this part (Figure A5)
.  
. *Basic
.  
. sort cem_strata_basic
{txt}
{com}. bysort cem_strata_basic: gen id_strat_basic=_n
{txt}
{com}. 
. bysort cem_strata_basic: egen PSmean1=mean(PS)
{txt}(2015 missing values generated)

{com}. replace PSmean1=. if cem_basic==0
{txt}(15,074 real changes made, 15,074 to missing)

{com}.  
.  **Augmented
.  
. sort cem_strata_augmented
{txt}
{com}. bysort cem_strata_augmented: gen id_strat_augmented=_n
{txt}
{com}. 
. bysort cem_strata_augmented: egen PSmean2=mean(PS)
{txt}(4234 missing values generated)

{com}. replace PSmean2=. if cem_augmented==0
{txt}(26,050 real changes made, 26,050 to missing)

{com}. 
. **Full
.  
. sort cem_strata_full
{txt}
{com}. bysort cem_strata_full: gen id_strat_full=_n
{txt}
{com}. 
. bysort cem_strata_full: egen PSmean3=mean(PS)
{txt}(6563 missing values generated)

{com}. replace PSmean3=. if cem_full==0
{txt}(35,312 real changes made, 35,312 to missing)

{com}. 
.  
. *******************************************************************************************
. * Creating Varibles for Table 6 in Appendix (Out-of Sample prediction validation)
. *******************************************************************************************
. 
. 
. *Test group completely random
. 
. * First step: we create a variable that is a random number, but only for the people who vote
. 
. set seed 234567
{txt}
{com}. 
. gen randoma=runiform(0,1) if voting==1  
{txt}(11,137 missing values generated)

{com}. 
. * Second step: we use this random number to pick 3000 voters at random among all voters
. * This variable is called "inclusion" (0=NOT in the sample 3000 voters, 1=in sample the 3000 voters)
. 
. sort randoma
{txt}
{com}. gen inclusiona=0 if voting==1
{txt}(11,137 missing values generated)

{com}. replace inclusiona=1 if _n<3001 & voting==1
{txt}(3,000 real changes made)

{com}. 
. * Third step: we create alternative vote variables. It's the same than the original one
. * Except for people in the sample of 3000 voters. For them we remove the vote variables, and put missing values
. 
. gen voting2a=voting
{txt}
{com}. replace voting2a=0 if inclusiona==1
{txt}(3,000 real changes made)

{com}. 
. gen PS2a=PS
{txt}(11,137 missing values generated)

{com}. replace PS2a=. if inclusiona==1
{txt}(3,000 real changes made, 3,000 to missing)

{com}. 
. gen left2a=left
{txt}(11,137 missing values generated)

{com}. replace left2a=. if inclusiona==1
{txt}(3,000 real changes made, 3,000 to missing)

{com}. 
. gen green2a=green
{txt}(11,137 missing values generated)

{com}. replace green2a=. if inclusiona==1
{txt}(3,000 real changes made, 3,000 to missing)

{com}. 
. * Fourth step: let's now see whether we can recover the vote variable in the sample of 3000 voters.
. 
. * 1. With a normal CEM (we exclude real abstainers)
. 
. cem education agea (24 34 44 54 64) female bebe minority y uemp3m source(#0) polintr election(#0) if voting==1, treatment(voting2a)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}26534
{txt}Number of matched strata: {res}1482

           {txt}    0      1
      All  {res} 3000  41900
{txt}  Matched  {res} 1722   5889
{txt}Unmatched  {res} 1278  36011


{txt}Multivariate L1 distance: {res}.69456614

{txt}Univariate imbalance:

                 L1      mean       min       25%       50%       75%       max
education  {res} 1.9e-15   2.0e-15         0         0         0         0         .
{txt}     agea  {res}  .07683   -.01205         1         0         0         0         3
{txt}   female  {res} 1.7e-16   5.8e-15         0         0         0         0         0
{txt}     bebe  {res} 9.2e-16   5.1e-15         0         0         0         0         0
{txt} minority  {res} 5.7e-17         0         0         0         0         0         0
{txt}        y  {res} 4.8e-16   5.3e-15         0         0         0         0         .
{txt}   uemp3m  {res} 7.2e-16   2.7e-15         0         0         0         0         0
{txt}   source  {res} 7.2e-16   3.1e-15         0         0         0         0         .
{txt}  polintr  {res} 8.1e-16  -4.9e-15         0         0         0         0         .
{txt} election  {res} 1.7e-15   4.4e-14         0         0         0         0         0
{txt}
{com}. 
. 
. * We compare the vote PS in the sample of 3000 vooters, in reality and with CEM
. 
. 
. * 2. With normal logit
. 
. logit PS2 education agea female bebe minority y uemp3m i.source polintr i.election

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-22941.827}  
Iteration 1:{space 3}log likelihood = {res:-22142.917}  
Iteration 2:{space 3}log likelihood = {res:-22120.076}  
Iteration 3:{space 3}log likelihood = {res:-22119.935}  
Iteration 4:{space 3}log likelihood = {res:-22119.935}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    40,569
{txt}{col 49}LR chi2({res}40{txt}){col 67}= {res}   1643.78
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-22119.935{txt}{col 49}Pseudo R2{col 67}= {res}    0.0358

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        PS2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2}-.1161799{col 26}{space 2} .0101612{col 37}{space 1}  -11.43{col 46}{space 3}0.000{col 54}{space 4}-.1360955{col 67}{space 3}-.0962644
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0027916{col 26}{space 2} .0009723{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0008859{col 67}{space 3} .0046973
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0551326{col 26}{space 2} .0237984{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4} .0084887{col 67}{space 3} .1017766
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}-.0010976{col 26}{space 2} .0262338{col 37}{space 1}   -0.04{col 46}{space 3}0.967{col 54}{space 4}-.0525148{col 67}{space 3} .0503196
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.5965267{col 26}{space 2} .0672414{col 37}{space 1}   -8.87{col 46}{space 3}0.000{col 54}{space 4}-.7283174{col 67}{space 3}-.4647359
{txt}{space 11}y {c |}{col 14}{res}{space 2} .0272693{col 26}{space 2} .0168747{col 37}{space 1}    1.62{col 46}{space 3}0.106{col 54}{space 4}-.0058044{col 67}{space 3} .0603431
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.1391033{col 26}{space 2} .0275561{col 37}{space 1}   -5.05{col 46}{space 3}0.000{col 54}{space 4}-.1931123{col 67}{space 3}-.0850943
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5084399{col 26}{space 2} .0435204{col 37}{space 1}   11.68{col 46}{space 3}0.000{col 54}{space 4} .4231415{col 67}{space 3} .5937384
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5014748{col 26}{space 2} .0702371{col 37}{space 1}    7.14{col 46}{space 3}0.000{col 54}{space 4} .3638127{col 67}{space 3} .6391369
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4861421{col 26}{space 2}  .050834{col 37}{space 1}    9.56{col 46}{space 3}0.000{col 54}{space 4} .3865092{col 67}{space 3}  .585775
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.0825079{col 26}{space 2} .0143562{col 37}{space 1}   -5.75{col 46}{space 3}0.000{col 54}{space 4}-.1106456{col 67}{space 3}-.0543703
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.2500917{col 26}{space 2} .1177599{col 37}{space 1}   -2.12{col 46}{space 3}0.034{col 54}{space 4}-.4808968{col 67}{space 3}-.0192865
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4823511{col 26}{space 2} .0958424{col 37}{space 1}   -5.03{col 46}{space 3}0.000{col 54}{space 4}-.6701988{col 67}{space 3}-.2945035
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5961627{col 26}{space 2} .0838498{col 37}{space 1}    7.11{col 46}{space 3}0.000{col 54}{space 4}   .43182{col 67}{space 3} .7605053
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .6823485{col 26}{space 2} .0832859{col 37}{space 1}    8.19{col 46}{space 3}0.000{col 54}{space 4}  .519111{col 67}{space 3} .8455859
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.3679579{col 26}{space 2} .0921315{col 37}{space 1}   -3.99{col 46}{space 3}0.000{col 54}{space 4}-.5485323{col 67}{space 3}-.1873835
{txt}{space 10}7  {c |}{col 14}{res}{space 2} -1.28118{col 26}{space 2} .1203652{col 37}{space 1}  -10.64{col 46}{space 3}0.000{col 54}{space 4}-1.517091{col 67}{space 3}-1.045268
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.4309173{col 26}{space 2} .1124872{col 37}{space 1}   -3.83{col 46}{space 3}0.000{col 54}{space 4}-.6513881{col 67}{space 3}-.2104465
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1311164{col 26}{space 2}  .088843{col 37}{space 1}    1.48{col 46}{space 3}0.140{col 54}{space 4}-.0430125{col 67}{space 3} .3052454
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0260601{col 26}{space 2} .0910484{col 37}{space 1}   -0.29{col 46}{space 3}0.775{col 54}{space 4}-.2045117{col 67}{space 3} .1523915
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.1400729{col 26}{space 2} .0835256{col 37}{space 1}   -1.68{col 46}{space 3}0.094{col 54}{space 4}-.3037801{col 67}{space 3} .0236343
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1095887{col 26}{space 2} .0893657{col 37}{space 1}    1.23{col 46}{space 3}0.220{col 54}{space 4}-.0655648{col 67}{space 3} .2847421
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3158201{col 26}{space 2} .0887145{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .1419429{col 67}{space 3} .4896973
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.1409287{col 26}{space 2} .0926502{col 37}{space 1}   -1.52{col 46}{space 3}0.128{col 54}{space 4}-.3225197{col 67}{space 3} .0406623
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.5541895{col 26}{space 2} .1092659{col 37}{space 1}   -5.07{col 46}{space 3}0.000{col 54}{space 4}-.7683467{col 67}{space 3}-.3400324
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3587138{col 26}{space 2} .0866446{col 37}{space 1}    4.14{col 46}{space 3}0.000{col 54}{space 4} .1888935{col 67}{space 3} .5285341
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .0788015{col 26}{space 2} .0869431{col 37}{space 1}    0.91{col 46}{space 3}0.365{col 54}{space 4}-.0916038{col 67}{space 3} .2492067
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2486137{col 26}{space 2} .0866682{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0787471{col 67}{space 3} .4184802
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.0214298{col 26}{space 2}  .091006{col 37}{space 1}   -0.24{col 46}{space 3}0.814{col 54}{space 4}-.1997983{col 67}{space 3} .1569388
{txt}{space 9}21  {c |}{col 14}{res}{space 2} -.161454{col 26}{space 2}   .09446{col 37}{space 1}   -1.71{col 46}{space 3}0.087{col 54}{space 4}-.3465922{col 67}{space 3} .0236841
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.5768058{col 26}{space 2} .0932556{col 37}{space 1}   -6.19{col 46}{space 3}0.000{col 54}{space 4}-.7595834{col 67}{space 3}-.3940283
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.8368996{col 26}{space 2} .0978017{col 37}{space 1}   -8.56{col 46}{space 3}0.000{col 54}{space 4}-1.028587{col 67}{space 3}-.6452118
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.1900265{col 26}{space 2} .0915152{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4} -.369393{col 67}{space 3}-.0106601
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .0734457{col 26}{space 2} .1126958{col 37}{space 1}    0.65{col 46}{space 3}0.515{col 54}{space 4}-.1474341{col 67}{space 3} .2943255
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.1994041{col 26}{space 2} .0949714{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-.3855446{col 67}{space 3}-.0132636
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .0676031{col 26}{space 2} .0921396{col 37}{space 1}    0.73{col 46}{space 3}0.463{col 54}{space 4}-.1129872{col 67}{space 3} .2481934
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.5362424{col 26}{space 2} .0984835{col 37}{space 1}   -5.44{col 46}{space 3}0.000{col 54}{space 4}-.7292664{col 67}{space 3}-.3432183
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.0458513{col 26}{space 2} .0816473{col 37}{space 1}   -0.56{col 46}{space 3}0.574{col 54}{space 4}-.2058771{col 67}{space 3} .1141746
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1258256{col 26}{space 2} .0937855{col 37}{space 1}    1.34{col 46}{space 3}0.180{col 54}{space 4}-.0579905{col 67}{space 3} .3096418
{txt}{space 9}31  {c |}{col 14}{res}{space 2}  .085395{col 26}{space 2} .0880049{col 37}{space 1}    0.97{col 46}{space 3}0.332{col 54}{space 4}-.0870915{col 67}{space 3} .2578815
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0994364{col 26}{space 2} .1822978{col 37}{space 1}    0.55{col 46}{space 3}0.585{col 54}{space 4}-.2578608{col 67}{space 3} .4567335
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict PS2logita
{txt}(option {bf:pr} assumed; Pr(PS2a))
(2,083 missing values generated)

{com}. 
. logit left2 education agea female bebe minority   y uemp3m i.source  polintr    i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1497 obs not used

note: 7.election != 0 predicts failure perfectly
      7.election dropped and 1197 obs not used

note: 9.election != 0 predicts failure perfectly
      9.election dropped and 1369 obs not used

note: 15.election != 0 predicts failure perfectly
      15.election dropped and 1309 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1475 obs not used

note: 23.election != 0 predicts failure perfectly
      23.election dropped and 1658 obs not used

note: 27.election != 0 predicts failure perfectly
      27.election dropped and 1178 obs not used

note: 31.election != 0 predicts failure perfectly
      31.election dropped and 1399 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-7971.1135}  
Iteration 1:{space 3}log likelihood = {res:-7529.7097}  
Iteration 2:{space 3}log likelihood = {res:-7413.1839}  
Iteration 3:{space 3}log likelihood = {res:-7411.2179}  
Iteration 4:{space 3}log likelihood = {res:-7411.1891}  
Iteration 5:{space 3}log likelihood = {res:-7411.1891}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,487
{txt}{col 49}LR chi2({res}32{txt}){col 67}= {res}   1119.85
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7411.1891{txt}{col 49}Pseudo R2{col 67}= {res}    0.0702

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      left2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .0552273{col 26}{space 2} .0197601{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} .0164982{col 67}{space 3} .0939564
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0084873{col 26}{space 2} .0018226{col 37}{space 1}   -4.66{col 46}{space 3}0.000{col 54}{space 4}-.0120595{col 67}{space 3}-.0049151
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0026785{col 26}{space 2} .0456556{col 37}{space 1}   -0.06{col 46}{space 3}0.953{col 54}{space 4}-.0921619{col 67}{space 3} .0868049
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .1814221{col 26}{space 2} .0494187{col 37}{space 1}    3.67{col 46}{space 3}0.000{col 54}{space 4} .0845631{col 67}{space 3}  .278281
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0346793{col 26}{space 2} .1361567{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4}-.3015414{col 67}{space 3} .2321829
{txt}{space 11}y {c |}{col 14}{res}{space 2} .2014971{col 26}{space 2} .0318632{col 37}{space 1}    6.32{col 46}{space 3}0.000{col 54}{space 4} .1390465{col 67}{space 3} .2639478
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.3773846{col 26}{space 2} .0489352{col 37}{space 1}   -7.71{col 46}{space 3}0.000{col 54}{space 4}-.4732958{col 67}{space 3}-.2814734
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3735387{col 26}{space 2} .0801853{col 37}{space 1}    4.66{col 46}{space 3}0.000{col 54}{space 4} .2163783{col 67}{space 3}  .530699
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6270802{col 26}{space 2} .1222233{col 37}{space 1}    5.13{col 46}{space 3}0.000{col 54}{space 4} .3875269{col 67}{space 3} .8666335
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1877964{col 26}{space 2} .0989894{col 37}{space 1}    1.90{col 46}{space 3}0.058{col 54}{space 4}-.0062192{col 67}{space 3} .3818121
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.2392142{col 26}{space 2} .0284422{col 37}{space 1}   -8.41{col 46}{space 3}0.000{col 54}{space 4}-.2949598{col 67}{space 3}-.1834686
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-2.206193{col 26}{space 2} .3472033{col 37}{space 1}   -6.35{col 46}{space 3}0.000{col 54}{space 4}-2.886699{col 67}{space 3}-1.525687
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4685988{col 26}{space 2} .1271539{col 37}{space 1}   -3.69{col 46}{space 3}0.000{col 54}{space 4}-.7178158{col 67}{space 3}-.2193818
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}-1.421898{col 26}{space 2} .1628914{col 37}{space 1}   -8.73{col 46}{space 3}0.000{col 54}{space 4} -1.74116{col 67}{space 3}-1.102637
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.9832281{col 26}{space 2} .1408298{col 37}{space 1}   -6.98{col 46}{space 3}0.000{col 54}{space 4}-1.259249{col 67}{space 3}-.7072068
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}8  {c |}{col 14}{res}{space 2}-1.076655{col 26}{space 2}  .189268{col 37}{space 1}   -5.69{col 46}{space 3}0.000{col 54}{space 4}-1.447614{col 67}{space 3}-.7056968
{txt}{space 10}9  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}10  {c |}{col 14}{res}{space 2}-1.452824{col 26}{space 2} .1822985{col 37}{space 1}   -7.97{col 46}{space 3}0.000{col 54}{space 4}-1.810123{col 67}{space 3}-1.095526
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.5113201{col 26}{space 2} .1139389{col 37}{space 1}   -4.49{col 46}{space 3}0.000{col 54}{space 4}-.7346362{col 67}{space 3} -.288004
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-1.179605{col 26}{space 2} .1560135{col 37}{space 1}   -7.56{col 46}{space 3}0.000{col 54}{space 4}-1.485386{col 67}{space 3}-.8738244
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.0839538{col 26}{space 2} .1223135{col 37}{space 1}   -0.69{col 46}{space 3}0.492{col 54}{space 4}-.3236838{col 67}{space 3} .1557763
{txt}{space 9}15  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}16  {c |}{col 14}{res}{space 2} -2.88699{col 26}{space 2}  .389018{col 37}{space 1}   -7.42{col 46}{space 3}0.000{col 54}{space 4}-3.649452{col 67}{space 3}-2.124529
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-1.118733{col 26}{space 2} .1528858{col 37}{space 1}   -7.32{col 46}{space 3}0.000{col 54}{space 4}-1.418384{col 67}{space 3}-.8190824
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2}-.4969439{col 26}{space 2} .1282451{col 37}{space 1}   -3.87{col 46}{space 3}0.000{col 54}{space 4}-.7482996{col 67}{space 3}-.2455882
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.3698387{col 26}{space 2} .1248767{col 37}{space 1}   -2.96{col 46}{space 3}0.003{col 54}{space 4}-.6145925{col 67}{space 3}-.1250848
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.7791338{col 26}{space 2} .1434546{col 37}{space 1}   -5.43{col 46}{space 3}0.000{col 54}{space 4}  -1.0603{col 67}{space 3}-.4979679
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-1.027143{col 26}{space 2} .1373033{col 37}{space 1}   -7.48{col 46}{space 3}0.000{col 54}{space 4}-1.296253{col 67}{space 3}-.7580338
{txt}{space 9}23  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}24  {c |}{col 14}{res}{space 2}-.4721066{col 26}{space 2} .1270536{col 37}{space 1}   -3.72{col 46}{space 3}0.000{col 54}{space 4} -.721127{col 67}{space 3}-.2230862
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-1.192086{col 26}{space 2} .2068774{col 37}{space 1}   -5.76{col 46}{space 3}0.000{col 54}{space 4}-1.597558{col 67}{space 3}-.7866135
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-1.252906{col 26}{space 2} .1718422{col 37}{space 1}   -7.29{col 46}{space 3}0.000{col 54}{space 4}-1.589711{col 67}{space 3}-.9161014
{txt}{space 9}27  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}28  {c |}{col 14}{res}{space 2} .5235917{col 26}{space 2} .1089968{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .3099619{col 67}{space 3} .7372215
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.2728272{col 26}{space 2} .1074322{col 37}{space 1}   -2.54{col 46}{space 3}0.011{col 54}{space 4}-.4833905{col 67}{space 3}-.0622639
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-1.188824{col 26}{space 2}  .167936{col 37}{space 1}   -7.08{col 46}{space 3}0.000{col 54}{space 4}-1.517972{col 67}{space 3}-.8596752
{txt}{space 9}31  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.262162{col 26}{space 2} .3443834{col 37}{space 1}   -3.66{col 46}{space 3}0.000{col 54}{space 4}-1.937142{col 67}{space 3}-.5871834
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict left2logita
{txt}(option {bf:pr} assumed; Pr(left2a))
(17,177 missing values generated)

{com}. 
. logit green2 education agea female bebe minority   y uemp3m  i.source  polintr   i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1497 obs not used

note: 5.election != 0 predicts failure perfectly
      5.election dropped and 1658 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1475 obs not used

note: 21.election != 0 predicts failure perfectly
      21.election dropped and 1227 obs not used

note: 25.election != 0 predicts failure perfectly
      25.election dropped and 601 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-8325.5737}  
Iteration 1:{space 3}log likelihood = {res:-7652.8589}  
Iteration 2:{space 3}log likelihood = {res:-7527.1668}  
Iteration 3:{space 3}log likelihood = {res:-7525.4287}  
Iteration 4:{space 3}log likelihood = {res:-7525.4209}  
Iteration 5:{space 3}log likelihood = {res:-7525.4209}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    34,111
{txt}{col 49}LR chi2({res}35{txt}){col 67}= {res}   1600.31
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7525.4209{txt}{col 49}Pseudo R2{col 67}= {res}    0.0961

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     green2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .3323139{col 26}{space 2} .0210809{col 37}{space 1}   15.76{col 46}{space 3}0.000{col 54}{space 4} .2909961{col 67}{space 3} .3736317
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0168519{col 26}{space 2} .0018378{col 37}{space 1}   -9.17{col 46}{space 3}0.000{col 54}{space 4}-.0204539{col 67}{space 3}  -.01325
{txt}{space 6}female {c |}{col 14}{res}{space 2} .2487313{col 26}{space 2} .0458147{col 37}{space 1}    5.43{col 46}{space 3}0.000{col 54}{space 4} .1589362{col 67}{space 3} .3385265
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0514987{col 26}{space 2} .0485687{col 37}{space 1}    1.06{col 46}{space 3}0.289{col 54}{space 4}-.0436942{col 67}{space 3} .1466916
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0788908{col 26}{space 2} .1360707{col 37}{space 1}   -0.58{col 46}{space 3}0.562{col 54}{space 4}-.3455844{col 67}{space 3} .1878027
{txt}{space 11}y {c |}{col 14}{res}{space 2} .0174466{col 26}{space 2} .0337409{col 37}{space 1}    0.52{col 46}{space 3}0.605{col 54}{space 4}-.0486843{col 67}{space 3} .0835775
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.2300301{col 26}{space 2} .0509535{col 37}{space 1}   -4.51{col 46}{space 3}0.000{col 54}{space 4}-.3298972{col 67}{space 3}-.1301631
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1448573{col 26}{space 2} .0723111{col 37}{space 1}   -2.00{col 46}{space 3}0.045{col 54}{space 4}-.2865845{col 67}{space 3}-.0031301
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1524335{col 26}{space 2} .1209123{col 37}{space 1}    1.26{col 46}{space 3}0.207{col 54}{space 4}-.0845503{col 67}{space 3} .3894172
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1655355{col 26}{space 2} .0967963{col 37}{space 1}   -1.71{col 46}{space 3}0.087{col 54}{space 4}-.3552528{col 67}{space 3} .0241818
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.2042119{col 26}{space 2} .0293717{col 37}{space 1}   -6.95{col 46}{space 3}0.000{col 54}{space 4}-.2617794{col 67}{space 3}-.1466444
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.701201{col 26}{space 2} .3052649{col 37}{space 1}    5.57{col 46}{space 3}0.000{col 54}{space 4} 1.102892{col 67}{space 3} 2.299509
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.966421{col 26}{space 2} .2664679{col 37}{space 1}    7.38{col 46}{space 3}0.000{col 54}{space 4} 1.444153{col 67}{space 3} 2.488688
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}6  {c |}{col 14}{res}{space 2} 2.060352{col 26}{space 2} .2634776{col 37}{space 1}    7.82{col 46}{space 3}0.000{col 54}{space 4} 1.543946{col 67}{space 3} 2.576759
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .6899104{col 26}{space 2} .3080345{col 37}{space 1}    2.24{col 46}{space 3}0.025{col 54}{space 4} .0861739{col 67}{space 3} 1.293647
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .5981702{col 26}{space 2}  .366243{col 37}{space 1}    1.63{col 46}{space 3}0.102{col 54}{space 4}-.1196529{col 67}{space 3} 1.315993
{txt}{space 10}9  {c |}{col 14}{res}{space 2} 2.541947{col 26}{space 2} .2593765{col 37}{space 1}    9.80{col 46}{space 3}0.000{col 54}{space 4} 2.033579{col 67}{space 3} 3.050316
{txt}{space 9}10  {c |}{col 14}{res}{space 2}  1.60909{col 26}{space 2} .2941782{col 37}{space 1}    5.47{col 46}{space 3}0.000{col 54}{space 4} 1.032511{col 67}{space 3} 2.185669
{txt}{space 9}11  {c |}{col 14}{res}{space 2} 2.460688{col 26}{space 2} .2545565{col 37}{space 1}    9.67{col 46}{space 3}0.000{col 54}{space 4} 1.961766{col 67}{space 3} 2.959609
{txt}{space 9}12  {c |}{col 14}{res}{space 2} 1.418138{col 26}{space 2} .2791266{col 37}{space 1}    5.08{col 46}{space 3}0.000{col 54}{space 4} .8710597{col 67}{space 3} 1.965216
{txt}{space 9}13  {c |}{col 14}{res}{space 2} 1.210225{col 26}{space 2} .2869596{col 37}{space 1}    4.22{col 46}{space 3}0.000{col 54}{space 4}  .647794{col 67}{space 3} 1.772655
{txt}{space 9}15  {c |}{col 14}{res}{space 2} 2.708719{col 26}{space 2} .2582677{col 37}{space 1}   10.49{col 46}{space 3}0.000{col 54}{space 4} 2.202524{col 67}{space 3} 3.214914
{txt}{space 9}16  {c |}{col 14}{res}{space 2} 2.045465{col 26}{space 2} .2781588{col 37}{space 1}    7.35{col 46}{space 3}0.000{col 54}{space 4} 1.500283{col 67}{space 3} 2.590646
{txt}{space 9}17  {c |}{col 14}{res}{space 2} 1.462822{col 26}{space 2}  .276834{col 37}{space 1}    5.28{col 46}{space 3}0.000{col 54}{space 4} .9202375{col 67}{space 3} 2.005407
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2} .6403047{col 26}{space 2} .3058984{col 37}{space 1}    2.09{col 46}{space 3}0.036{col 54}{space 4} .0407549{col 67}{space 3} 1.239854
{txt}{space 9}20  {c |}{col 14}{res}{space 2} 1.605241{col 26}{space 2} .2719505{col 37}{space 1}    5.90{col 46}{space 3}0.000{col 54}{space 4} 1.072228{col 67}{space 3} 2.138254
{txt}{space 9}21  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}22  {c |}{col 14}{res}{space 2}  2.19145{col 26}{space 2} .2601811{col 37}{space 1}    8.42{col 46}{space 3}0.000{col 54}{space 4} 1.681505{col 67}{space 3} 2.701396
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .6350969{col 26}{space 2} .3015188{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0441309{col 67}{space 3} 1.226063
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .8282479{col 26}{space 2} .2994025{col 37}{space 1}    2.77{col 46}{space 3}0.006{col 54}{space 4} .2414297{col 67}{space 3} 1.415066
{txt}{space 9}25  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}26  {c |}{col 14}{res}{space 2} 1.786905{col 26}{space 2} .2878952{col 37}{space 1}    6.21{col 46}{space 3}0.000{col 54}{space 4} 1.222641{col 67}{space 3} 2.351169
{txt}{space 9}27  {c |}{col 14}{res}{space 2} 2.640154{col 26}{space 2} .2613666{col 37}{space 1}   10.10{col 46}{space 3}0.000{col 54}{space 4} 2.127885{col 67}{space 3} 3.152423
{txt}{space 9}28  {c |}{col 14}{res}{space 2} 2.254034{col 26}{space 2} .2623633{col 37}{space 1}    8.59{col 46}{space 3}0.000{col 54}{space 4} 1.739811{col 67}{space 3} 2.768256
{txt}{space 9}29  {c |}{col 14}{res}{space 2}  2.09262{col 26}{space 2} .2560919{col 37}{space 1}    8.17{col 46}{space 3}0.000{col 54}{space 4} 1.590689{col 67}{space 3} 2.594551
{txt}{space 9}30  {c |}{col 14}{res}{space 2} 1.776076{col 26}{space 2} .2768205{col 37}{space 1}    6.42{col 46}{space 3}0.000{col 54}{space 4} 1.233518{col 67}{space 3} 2.318634
{txt}{space 9}31  {c |}{col 14}{res}{space 2} 2.092057{col 26}{space 2} .2620026{col 37}{space 1}    7.98{col 46}{space 3}0.000{col 54}{space 4} 1.578541{col 67}{space 3} 2.605572
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-3.869802{col 26}{space 2} .4173105{col 37}{space 1}   -9.27{col 46}{space 3}0.000{col 54}{space 4}-4.687715{col 67}{space 3}-3.051888
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict green2logita
{txt}(option {bf:pr} assumed; Pr(green2a))
(11,197 missing values generated)

{com}. 
. *3. With logit with simulated turnout, it's the same than normal logit except that we remove likely abstainers
. 
. logit voting2a  education agea female bebe minority y uemp3m i.source  polintr i.election if voting==1 // A regression predicting turnout

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-10682.078}  
Iteration 1:{space 3}log likelihood = {res:-10657.641}  
Iteration 2:{space 3}log likelihood = {res:-10657.479}  
Iteration 3:{space 3}log likelihood = {res:-10657.479}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    43,480
{txt}{col 49}LR chi2({res}40{txt}){col 67}= {res}     49.20
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1511
{txt}Log likelihood = {res}-10657.479{txt}{col 49}Pseudo R2{col 67}= {res}    0.0023

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    voting2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .0020475{col 26}{space 2} .0165812{col 37}{space 1}    0.12{col 46}{space 3}0.902{col 54}{space 4}-.0304511{col 67}{space 3} .0345461
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0004278{col 26}{space 2} .0015797{col 37}{space 1}   -0.27{col 46}{space 3}0.787{col 54}{space 4}-.0035239{col 67}{space 3} .0026684
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0107949{col 26}{space 2} .0391837{col 37}{space 1}   -0.28{col 46}{space 3}0.783{col 54}{space 4}-.0875935{col 67}{space 3} .0660036
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}-.0697376{col 26}{space 2} .0432417{col 37}{space 1}   -1.61{col 46}{space 3}0.107{col 54}{space 4}-.1544899{col 67}{space 3} .0150146
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.1868348{col 26}{space 2} .1321727{col 37}{space 1}   -1.41{col 46}{space 3}0.157{col 54}{space 4}-.4458884{col 67}{space 3} .0722189
{txt}{space 11}y {c |}{col 14}{res}{space 2}-.0336028{col 26}{space 2} .0278929{col 37}{space 1}   -1.20{col 46}{space 3}0.228{col 54}{space 4}-.0882719{col 67}{space 3} .0210663
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}  .028676{col 26}{space 2} .0458653{col 37}{space 1}    0.63{col 46}{space 3}0.532{col 54}{space 4}-.0612184{col 67}{space 3} .1185703
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0454052{col 26}{space 2} .0664215{col 37}{space 1}   -0.68{col 46}{space 3}0.494{col 54}{space 4}-.1755889{col 67}{space 3} .0847786
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1749614{col 26}{space 2}  .107491{col 37}{space 1}   -1.63{col 46}{space 3}0.104{col 54}{space 4}-.3856398{col 67}{space 3}  .035717
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0246221{col 26}{space 2} .0797221{col 37}{space 1}   -0.31{col 46}{space 3}0.757{col 54}{space 4}-.1808745{col 67}{space 3} .1316303
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2} .0496871{col 26}{space 2} .0237971{col 37}{space 1}    2.09{col 46}{space 3}0.037{col 54}{space 4} .0030455{col 67}{space 3} .0963286
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}  -.04194{col 26}{space 2} .1709131{col 37}{space 1}   -0.25{col 46}{space 3}0.806{col 54}{space 4}-.3769234{col 67}{space 3} .2930435
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1911423{col 26}{space 2} .1448736{col 37}{space 1}    1.32{col 46}{space 3}0.187{col 54}{space 4}-.0928048{col 67}{space 3} .4750894
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1446747{col 26}{space 2} .1398777{col 37}{space 1}    1.03{col 46}{space 3}0.301{col 54}{space 4}-.1294805{col 67}{space 3} .4188298
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1077838{col 26}{space 2} .1382093{col 37}{space 1}    0.78{col 46}{space 3}0.435{col 54}{space 4}-.1631014{col 67}{space 3} .3786691
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2230653{col 26}{space 2} .1425755{col 37}{space 1}    1.56{col 46}{space 3}0.118{col 54}{space 4}-.0563775{col 67}{space 3} .5025081
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2734506{col 26}{space 2} .1530253{col 37}{space 1}    1.79{col 46}{space 3}0.074{col 54}{space 4}-.0264736{col 67}{space 3} .5733747
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1133483{col 26}{space 2} .1658417{col 37}{space 1}    0.68{col 46}{space 3}0.494{col 54}{space 4}-.2116954{col 67}{space 3} .4383921
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1609441{col 26}{space 2} .1438757{col 37}{space 1}    1.12{col 46}{space 3}0.263{col 54}{space 4} -.121047{col 67}{space 3} .4429353
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0370766{col 26}{space 2} .1441732{col 37}{space 1}    0.26{col 46}{space 3}0.797{col 54}{space 4}-.2454977{col 67}{space 3} .3196508
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .2833271{col 26}{space 2} .1337655{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .0211515{col 67}{space 3} .5455026
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0895339{col 26}{space 2} .1430057{col 37}{space 1}    0.63{col 46}{space 3}0.531{col 54}{space 4}-.1907521{col 67}{space 3} .3698198
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .2416552{col 26}{space 2} .1484798{col 37}{space 1}    1.63{col 46}{space 3}0.104{col 54}{space 4}-.0493599{col 67}{space 3} .5326704
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .0965509{col 26}{space 2} .1438067{col 37}{space 1}    0.67{col 46}{space 3}0.502{col 54}{space 4} -.185305{col 67}{space 3} .3784067
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .2903873{col 26}{space 2} .1679728{col 37}{space 1}    1.73{col 46}{space 3}0.084{col 54}{space 4}-.0388333{col 67}{space 3} .6196079
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-.0272261{col 26}{space 2}   .13788{col 37}{space 1}   -0.20{col 46}{space 3}0.843{col 54}{space 4}-.2974659{col 67}{space 3} .2430137
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .1412984{col 26}{space 2} .1405338{col 37}{space 1}    1.01{col 46}{space 3}0.315{col 54}{space 4}-.1341428{col 67}{space 3} .4167395
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .3928446{col 26}{space 2} .1480326{col 37}{space 1}    2.65{col 46}{space 3}0.008{col 54}{space 4} .1027061{col 67}{space 3}  .682983
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .0987074{col 26}{space 2} .1431372{col 37}{space 1}    0.69{col 46}{space 3}0.490{col 54}{space 4}-.1818363{col 67}{space 3} .3792511
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .4363112{col 26}{space 2} .1611883{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1203879{col 67}{space 3} .7522345
{txt}{space 9}22  {c |}{col 14}{res}{space 2}   .25314{col 26}{space 2} .1405124{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4}-.0222593{col 67}{space 3} .5285392
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .2551973{col 26}{space 2} .1407596{col 37}{space 1}    1.81{col 46}{space 3}0.070{col 54}{space 4}-.0206864{col 67}{space 3} .5310809
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .0426192{col 26}{space 2} .1395526{col 37}{space 1}    0.31{col 46}{space 3}0.760{col 54}{space 4} -.230899{col 67}{space 3} .3161373
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .4252136{col 26}{space 2}  .203299{col 37}{space 1}    2.09{col 46}{space 3}0.036{col 54}{space 4} .0267548{col 67}{space 3} .8236724
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .2490102{col 26}{space 2} .1547146{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4}-.0542249{col 67}{space 3} .5522452
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .1513172{col 26}{space 2} .1490157{col 37}{space 1}    1.02{col 46}{space 3}0.310{col 54}{space 4}-.1407482{col 67}{space 3} .4433825
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .2420051{col 26}{space 2} .1485821{col 37}{space 1}    1.63{col 46}{space 3}0.103{col 54}{space 4}-.0492104{col 67}{space 3} .5332207
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .2673196{col 26}{space 2} .1311353{col 37}{space 1}    2.04{col 46}{space 3}0.041{col 54}{space 4} .0102992{col 67}{space 3}   .52434
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .2412344{col 26}{space 2} .1569523{col 37}{space 1}    1.54{col 46}{space 3}0.124{col 54}{space 4}-.0663864{col 67}{space 3} .5488553
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .2766368{col 26}{space 2} .1463075{col 37}{space 1}    1.89{col 46}{space 3}0.059{col 54}{space 4}-.0101207{col 67}{space 3} .5633943
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 2.900212{col 26}{space 2} .3304862{col 37}{space 1}    8.78{col 46}{space 3}0.000{col 54}{space 4} 2.252471{col 67}{space 3} 3.547953
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict voting2logita
{txt}(option {bf:pr} assumed; Pr(voting2a))
(2,083 missing values generated)

{com}. 
. gen likely_abstainera=1 if voting2logita<0.5 // People who have a prediction of voting lower than 0.5 are likely abstainers
{txt}(56,037 missing values generated)

{com}. replace likely_abstainera=0 if voting2logita>=0.5
{txt}(56,037 real changes made)

{com}. 
. tab likely_abstainera if inclusiona==1 // Almost noone. But It's normmal, because everybody in the sample of 3000 voters voted. When you do it in Table 1, some will be likely abstainers, so this step matters.

{txt}likely_abst {c |}
     ainera {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,000      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      3,000      100.00
{txt}
{com}. 
. logit PS2a education agea female bebe minority y uemp3m i.source polintr i.election if likely_abstainera==0 // We simply exlcude those likely abstainers

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-22941.827}  
Iteration 1:{space 3}log likelihood = {res:-22142.917}  
Iteration 2:{space 3}log likelihood = {res:-22120.076}  
Iteration 3:{space 3}log likelihood = {res:-22119.935}  
Iteration 4:{space 3}log likelihood = {res:-22119.935}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    40,569
{txt}{col 49}LR chi2({res}40{txt}){col 67}= {res}   1643.78
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-22119.935{txt}{col 49}Pseudo R2{col 67}= {res}    0.0358

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        PS2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2}-.1161799{col 26}{space 2} .0101612{col 37}{space 1}  -11.43{col 46}{space 3}0.000{col 54}{space 4}-.1360955{col 67}{space 3}-.0962644
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0027916{col 26}{space 2} .0009723{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0008859{col 67}{space 3} .0046973
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0551326{col 26}{space 2} .0237984{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4} .0084887{col 67}{space 3} .1017766
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}-.0010976{col 26}{space 2} .0262338{col 37}{space 1}   -0.04{col 46}{space 3}0.967{col 54}{space 4}-.0525148{col 67}{space 3} .0503196
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.5965267{col 26}{space 2} .0672414{col 37}{space 1}   -8.87{col 46}{space 3}0.000{col 54}{space 4}-.7283174{col 67}{space 3}-.4647359
{txt}{space 11}y {c |}{col 14}{res}{space 2} .0272693{col 26}{space 2} .0168747{col 37}{space 1}    1.62{col 46}{space 3}0.106{col 54}{space 4}-.0058044{col 67}{space 3} .0603431
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.1391033{col 26}{space 2} .0275561{col 37}{space 1}   -5.05{col 46}{space 3}0.000{col 54}{space 4}-.1931123{col 67}{space 3}-.0850943
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5084399{col 26}{space 2} .0435204{col 37}{space 1}   11.68{col 46}{space 3}0.000{col 54}{space 4} .4231415{col 67}{space 3} .5937384
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5014748{col 26}{space 2} .0702371{col 37}{space 1}    7.14{col 46}{space 3}0.000{col 54}{space 4} .3638127{col 67}{space 3} .6391369
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4861421{col 26}{space 2}  .050834{col 37}{space 1}    9.56{col 46}{space 3}0.000{col 54}{space 4} .3865092{col 67}{space 3}  .585775
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.0825079{col 26}{space 2} .0143562{col 37}{space 1}   -5.75{col 46}{space 3}0.000{col 54}{space 4}-.1106456{col 67}{space 3}-.0543703
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.2500917{col 26}{space 2} .1177599{col 37}{space 1}   -2.12{col 46}{space 3}0.034{col 54}{space 4}-.4808968{col 67}{space 3}-.0192865
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4823511{col 26}{space 2} .0958424{col 37}{space 1}   -5.03{col 46}{space 3}0.000{col 54}{space 4}-.6701988{col 67}{space 3}-.2945035
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5961627{col 26}{space 2} .0838498{col 37}{space 1}    7.11{col 46}{space 3}0.000{col 54}{space 4}   .43182{col 67}{space 3} .7605053
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .6823485{col 26}{space 2} .0832859{col 37}{space 1}    8.19{col 46}{space 3}0.000{col 54}{space 4}  .519111{col 67}{space 3} .8455859
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.3679579{col 26}{space 2} .0921315{col 37}{space 1}   -3.99{col 46}{space 3}0.000{col 54}{space 4}-.5485323{col 67}{space 3}-.1873835
{txt}{space 10}7  {c |}{col 14}{res}{space 2} -1.28118{col 26}{space 2} .1203652{col 37}{space 1}  -10.64{col 46}{space 3}0.000{col 54}{space 4}-1.517091{col 67}{space 3}-1.045268
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.4309173{col 26}{space 2} .1124872{col 37}{space 1}   -3.83{col 46}{space 3}0.000{col 54}{space 4}-.6513881{col 67}{space 3}-.2104465
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1311164{col 26}{space 2}  .088843{col 37}{space 1}    1.48{col 46}{space 3}0.140{col 54}{space 4}-.0430125{col 67}{space 3} .3052454
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0260601{col 26}{space 2} .0910484{col 37}{space 1}   -0.29{col 46}{space 3}0.775{col 54}{space 4}-.2045117{col 67}{space 3} .1523915
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.1400729{col 26}{space 2} .0835256{col 37}{space 1}   -1.68{col 46}{space 3}0.094{col 54}{space 4}-.3037801{col 67}{space 3} .0236343
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1095887{col 26}{space 2} .0893657{col 37}{space 1}    1.23{col 46}{space 3}0.220{col 54}{space 4}-.0655648{col 67}{space 3} .2847421
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3158201{col 26}{space 2} .0887145{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .1419429{col 67}{space 3} .4896973
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.1409287{col 26}{space 2} .0926502{col 37}{space 1}   -1.52{col 46}{space 3}0.128{col 54}{space 4}-.3225197{col 67}{space 3} .0406623
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.5541895{col 26}{space 2} .1092659{col 37}{space 1}   -5.07{col 46}{space 3}0.000{col 54}{space 4}-.7683467{col 67}{space 3}-.3400324
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3587138{col 26}{space 2} .0866446{col 37}{space 1}    4.14{col 46}{space 3}0.000{col 54}{space 4} .1888935{col 67}{space 3} .5285341
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .0788015{col 26}{space 2} .0869431{col 37}{space 1}    0.91{col 46}{space 3}0.365{col 54}{space 4}-.0916038{col 67}{space 3} .2492067
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2486137{col 26}{space 2} .0866682{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0787471{col 67}{space 3} .4184802
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.0214298{col 26}{space 2}  .091006{col 37}{space 1}   -0.24{col 46}{space 3}0.814{col 54}{space 4}-.1997983{col 67}{space 3} .1569388
{txt}{space 9}21  {c |}{col 14}{res}{space 2} -.161454{col 26}{space 2}   .09446{col 37}{space 1}   -1.71{col 46}{space 3}0.087{col 54}{space 4}-.3465922{col 67}{space 3} .0236841
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.5768058{col 26}{space 2} .0932556{col 37}{space 1}   -6.19{col 46}{space 3}0.000{col 54}{space 4}-.7595834{col 67}{space 3}-.3940283
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.8368996{col 26}{space 2} .0978017{col 37}{space 1}   -8.56{col 46}{space 3}0.000{col 54}{space 4}-1.028587{col 67}{space 3}-.6452118
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.1900265{col 26}{space 2} .0915152{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4} -.369393{col 67}{space 3}-.0106601
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .0734457{col 26}{space 2} .1126958{col 37}{space 1}    0.65{col 46}{space 3}0.515{col 54}{space 4}-.1474341{col 67}{space 3} .2943255
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.1994041{col 26}{space 2} .0949714{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-.3855446{col 67}{space 3}-.0132636
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .0676031{col 26}{space 2} .0921396{col 37}{space 1}    0.73{col 46}{space 3}0.463{col 54}{space 4}-.1129872{col 67}{space 3} .2481934
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.5362424{col 26}{space 2} .0984835{col 37}{space 1}   -5.44{col 46}{space 3}0.000{col 54}{space 4}-.7292664{col 67}{space 3}-.3432183
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.0458513{col 26}{space 2} .0816473{col 37}{space 1}   -0.56{col 46}{space 3}0.574{col 54}{space 4}-.2058771{col 67}{space 3} .1141746
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1258256{col 26}{space 2} .0937855{col 37}{space 1}    1.34{col 46}{space 3}0.180{col 54}{space 4}-.0579905{col 67}{space 3} .3096418
{txt}{space 9}31  {c |}{col 14}{res}{space 2}  .085395{col 26}{space 2} .0880049{col 37}{space 1}    0.97{col 46}{space 3}0.332{col 54}{space 4}-.0870915{col 67}{space 3} .2578815
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0994364{col 26}{space 2} .1822978{col 37}{space 1}    0.55{col 46}{space 3}0.585{col 54}{space 4}-.2578608{col 67}{space 3} .4567335
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict PS2logit2a
{txt}(option {bf:pr} assumed; Pr(PS2a))
(2,083 missing values generated)

{com}. 
. logit left2a education agea female bebe minority   y uemp3m i.source  polintr    i.election if likely_abstainera==0 

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1497 obs not used

note: 7.election != 0 predicts failure perfectly
      7.election dropped and 1197 obs not used

note: 9.election != 0 predicts failure perfectly
      9.election dropped and 1369 obs not used

note: 15.election != 0 predicts failure perfectly
      15.election dropped and 1309 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1475 obs not used

note: 23.election != 0 predicts failure perfectly
      23.election dropped and 1658 obs not used

note: 27.election != 0 predicts failure perfectly
      27.election dropped and 1178 obs not used

note: 31.election != 0 predicts failure perfectly
      31.election dropped and 1399 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-7971.1135}  
Iteration 1:{space 3}log likelihood = {res:-7529.7097}  
Iteration 2:{space 3}log likelihood = {res:-7413.1839}  
Iteration 3:{space 3}log likelihood = {res:-7411.2179}  
Iteration 4:{space 3}log likelihood = {res:-7411.1891}  
Iteration 5:{space 3}log likelihood = {res:-7411.1891}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,487
{txt}{col 49}LR chi2({res}32{txt}){col 67}= {res}   1119.85
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7411.1891{txt}{col 49}Pseudo R2{col 67}= {res}    0.0702

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      left2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .0552273{col 26}{space 2} .0197601{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} .0164982{col 67}{space 3} .0939564
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0084873{col 26}{space 2} .0018226{col 37}{space 1}   -4.66{col 46}{space 3}0.000{col 54}{space 4}-.0120595{col 67}{space 3}-.0049151
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0026785{col 26}{space 2} .0456556{col 37}{space 1}   -0.06{col 46}{space 3}0.953{col 54}{space 4}-.0921619{col 67}{space 3} .0868049
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .1814221{col 26}{space 2} .0494187{col 37}{space 1}    3.67{col 46}{space 3}0.000{col 54}{space 4} .0845631{col 67}{space 3}  .278281
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0346793{col 26}{space 2} .1361567{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4}-.3015414{col 67}{space 3} .2321829
{txt}{space 11}y {c |}{col 14}{res}{space 2} .2014971{col 26}{space 2} .0318632{col 37}{space 1}    6.32{col 46}{space 3}0.000{col 54}{space 4} .1390465{col 67}{space 3} .2639478
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.3773846{col 26}{space 2} .0489352{col 37}{space 1}   -7.71{col 46}{space 3}0.000{col 54}{space 4}-.4732958{col 67}{space 3}-.2814734
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3735387{col 26}{space 2} .0801853{col 37}{space 1}    4.66{col 46}{space 3}0.000{col 54}{space 4} .2163783{col 67}{space 3}  .530699
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6270802{col 26}{space 2} .1222233{col 37}{space 1}    5.13{col 46}{space 3}0.000{col 54}{space 4} .3875269{col 67}{space 3} .8666335
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1877964{col 26}{space 2} .0989894{col 37}{space 1}    1.90{col 46}{space 3}0.058{col 54}{space 4}-.0062192{col 67}{space 3} .3818121
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.2392142{col 26}{space 2} .0284422{col 37}{space 1}   -8.41{col 46}{space 3}0.000{col 54}{space 4}-.2949598{col 67}{space 3}-.1834686
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-2.206193{col 26}{space 2} .3472033{col 37}{space 1}   -6.35{col 46}{space 3}0.000{col 54}{space 4}-2.886699{col 67}{space 3}-1.525687
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4685988{col 26}{space 2} .1271539{col 37}{space 1}   -3.69{col 46}{space 3}0.000{col 54}{space 4}-.7178158{col 67}{space 3}-.2193818
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}-1.421898{col 26}{space 2} .1628914{col 37}{space 1}   -8.73{col 46}{space 3}0.000{col 54}{space 4} -1.74116{col 67}{space 3}-1.102637
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.9832281{col 26}{space 2} .1408298{col 37}{space 1}   -6.98{col 46}{space 3}0.000{col 54}{space 4}-1.259249{col 67}{space 3}-.7072068
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}8  {c |}{col 14}{res}{space 2}-1.076655{col 26}{space 2}  .189268{col 37}{space 1}   -5.69{col 46}{space 3}0.000{col 54}{space 4}-1.447614{col 67}{space 3}-.7056968
{txt}{space 10}9  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}10  {c |}{col 14}{res}{space 2}-1.452824{col 26}{space 2} .1822985{col 37}{space 1}   -7.97{col 46}{space 3}0.000{col 54}{space 4}-1.810123{col 67}{space 3}-1.095526
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.5113201{col 26}{space 2} .1139389{col 37}{space 1}   -4.49{col 46}{space 3}0.000{col 54}{space 4}-.7346362{col 67}{space 3} -.288004
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-1.179605{col 26}{space 2} .1560135{col 37}{space 1}   -7.56{col 46}{space 3}0.000{col 54}{space 4}-1.485386{col 67}{space 3}-.8738244
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.0839538{col 26}{space 2} .1223135{col 37}{space 1}   -0.69{col 46}{space 3}0.492{col 54}{space 4}-.3236838{col 67}{space 3} .1557763
{txt}{space 9}15  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}16  {c |}{col 14}{res}{space 2} -2.88699{col 26}{space 2}  .389018{col 37}{space 1}   -7.42{col 46}{space 3}0.000{col 54}{space 4}-3.649452{col 67}{space 3}-2.124529
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-1.118733{col 26}{space 2} .1528858{col 37}{space 1}   -7.32{col 46}{space 3}0.000{col 54}{space 4}-1.418384{col 67}{space 3}-.8190824
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2}-.4969439{col 26}{space 2} .1282451{col 37}{space 1}   -3.87{col 46}{space 3}0.000{col 54}{space 4}-.7482996{col 67}{space 3}-.2455882
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.3698387{col 26}{space 2} .1248767{col 37}{space 1}   -2.96{col 46}{space 3}0.003{col 54}{space 4}-.6145925{col 67}{space 3}-.1250848
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.7791338{col 26}{space 2} .1434546{col 37}{space 1}   -5.43{col 46}{space 3}0.000{col 54}{space 4}  -1.0603{col 67}{space 3}-.4979679
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-1.027143{col 26}{space 2} .1373033{col 37}{space 1}   -7.48{col 46}{space 3}0.000{col 54}{space 4}-1.296253{col 67}{space 3}-.7580338
{txt}{space 9}23  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}24  {c |}{col 14}{res}{space 2}-.4721066{col 26}{space 2} .1270536{col 37}{space 1}   -3.72{col 46}{space 3}0.000{col 54}{space 4} -.721127{col 67}{space 3}-.2230862
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-1.192086{col 26}{space 2} .2068774{col 37}{space 1}   -5.76{col 46}{space 3}0.000{col 54}{space 4}-1.597558{col 67}{space 3}-.7866135
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-1.252906{col 26}{space 2} .1718422{col 37}{space 1}   -7.29{col 46}{space 3}0.000{col 54}{space 4}-1.589711{col 67}{space 3}-.9161014
{txt}{space 9}27  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}28  {c |}{col 14}{res}{space 2} .5235917{col 26}{space 2} .1089968{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .3099619{col 67}{space 3} .7372215
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.2728272{col 26}{space 2} .1074322{col 37}{space 1}   -2.54{col 46}{space 3}0.011{col 54}{space 4}-.4833905{col 67}{space 3}-.0622639
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-1.188824{col 26}{space 2}  .167936{col 37}{space 1}   -7.08{col 46}{space 3}0.000{col 54}{space 4}-1.517972{col 67}{space 3}-.8596752
{txt}{space 9}31  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.262162{col 26}{space 2} .3443834{col 37}{space 1}   -3.66{col 46}{space 3}0.000{col 54}{space 4}-1.937142{col 67}{space 3}-.5871834
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict left2logit2a
{txt}(option {bf:pr} assumed; Pr(left2a))
(17,177 missing values generated)

{com}. 
. logit green2a education agea female bebe minority   y uemp3m  i.source  polintr   i.election if likely_abstainera==0 

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1497 obs not used

note: 5.election != 0 predicts failure perfectly
      5.election dropped and 1658 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1475 obs not used

note: 21.election != 0 predicts failure perfectly
      21.election dropped and 1227 obs not used

note: 25.election != 0 predicts failure perfectly
      25.election dropped and 601 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-8325.5737}  
Iteration 1:{space 3}log likelihood = {res:-7652.8589}  
Iteration 2:{space 3}log likelihood = {res:-7527.1668}  
Iteration 3:{space 3}log likelihood = {res:-7525.4287}  
Iteration 4:{space 3}log likelihood = {res:-7525.4209}  
Iteration 5:{space 3}log likelihood = {res:-7525.4209}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    34,111
{txt}{col 49}LR chi2({res}35{txt}){col 67}= {res}   1600.31
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7525.4209{txt}{col 49}Pseudo R2{col 67}= {res}    0.0961

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     green2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .3323139{col 26}{space 2} .0210809{col 37}{space 1}   15.76{col 46}{space 3}0.000{col 54}{space 4} .2909961{col 67}{space 3} .3736317
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0168519{col 26}{space 2} .0018378{col 37}{space 1}   -9.17{col 46}{space 3}0.000{col 54}{space 4}-.0204539{col 67}{space 3}  -.01325
{txt}{space 6}female {c |}{col 14}{res}{space 2} .2487313{col 26}{space 2} .0458147{col 37}{space 1}    5.43{col 46}{space 3}0.000{col 54}{space 4} .1589362{col 67}{space 3} .3385265
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0514987{col 26}{space 2} .0485687{col 37}{space 1}    1.06{col 46}{space 3}0.289{col 54}{space 4}-.0436942{col 67}{space 3} .1466916
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0788908{col 26}{space 2} .1360707{col 37}{space 1}   -0.58{col 46}{space 3}0.562{col 54}{space 4}-.3455844{col 67}{space 3} .1878027
{txt}{space 11}y {c |}{col 14}{res}{space 2} .0174466{col 26}{space 2} .0337409{col 37}{space 1}    0.52{col 46}{space 3}0.605{col 54}{space 4}-.0486843{col 67}{space 3} .0835775
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.2300301{col 26}{space 2} .0509535{col 37}{space 1}   -4.51{col 46}{space 3}0.000{col 54}{space 4}-.3298972{col 67}{space 3}-.1301631
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1448573{col 26}{space 2} .0723111{col 37}{space 1}   -2.00{col 46}{space 3}0.045{col 54}{space 4}-.2865845{col 67}{space 3}-.0031301
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1524335{col 26}{space 2} .1209123{col 37}{space 1}    1.26{col 46}{space 3}0.207{col 54}{space 4}-.0845503{col 67}{space 3} .3894172
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1655355{col 26}{space 2} .0967963{col 37}{space 1}   -1.71{col 46}{space 3}0.087{col 54}{space 4}-.3552528{col 67}{space 3} .0241818
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.2042119{col 26}{space 2} .0293717{col 37}{space 1}   -6.95{col 46}{space 3}0.000{col 54}{space 4}-.2617794{col 67}{space 3}-.1466444
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.701201{col 26}{space 2} .3052649{col 37}{space 1}    5.57{col 46}{space 3}0.000{col 54}{space 4} 1.102892{col 67}{space 3} 2.299509
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.966421{col 26}{space 2} .2664679{col 37}{space 1}    7.38{col 46}{space 3}0.000{col 54}{space 4} 1.444153{col 67}{space 3} 2.488688
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}6  {c |}{col 14}{res}{space 2} 2.060352{col 26}{space 2} .2634776{col 37}{space 1}    7.82{col 46}{space 3}0.000{col 54}{space 4} 1.543946{col 67}{space 3} 2.576759
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .6899104{col 26}{space 2} .3080345{col 37}{space 1}    2.24{col 46}{space 3}0.025{col 54}{space 4} .0861739{col 67}{space 3} 1.293647
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .5981702{col 26}{space 2}  .366243{col 37}{space 1}    1.63{col 46}{space 3}0.102{col 54}{space 4}-.1196529{col 67}{space 3} 1.315993
{txt}{space 10}9  {c |}{col 14}{res}{space 2} 2.541947{col 26}{space 2} .2593765{col 37}{space 1}    9.80{col 46}{space 3}0.000{col 54}{space 4} 2.033579{col 67}{space 3} 3.050316
{txt}{space 9}10  {c |}{col 14}{res}{space 2}  1.60909{col 26}{space 2} .2941782{col 37}{space 1}    5.47{col 46}{space 3}0.000{col 54}{space 4} 1.032511{col 67}{space 3} 2.185669
{txt}{space 9}11  {c |}{col 14}{res}{space 2} 2.460688{col 26}{space 2} .2545565{col 37}{space 1}    9.67{col 46}{space 3}0.000{col 54}{space 4} 1.961766{col 67}{space 3} 2.959609
{txt}{space 9}12  {c |}{col 14}{res}{space 2} 1.418138{col 26}{space 2} .2791266{col 37}{space 1}    5.08{col 46}{space 3}0.000{col 54}{space 4} .8710597{col 67}{space 3} 1.965216
{txt}{space 9}13  {c |}{col 14}{res}{space 2} 1.210225{col 26}{space 2} .2869596{col 37}{space 1}    4.22{col 46}{space 3}0.000{col 54}{space 4}  .647794{col 67}{space 3} 1.772655
{txt}{space 9}15  {c |}{col 14}{res}{space 2} 2.708719{col 26}{space 2} .2582677{col 37}{space 1}   10.49{col 46}{space 3}0.000{col 54}{space 4} 2.202524{col 67}{space 3} 3.214914
{txt}{space 9}16  {c |}{col 14}{res}{space 2} 2.045465{col 26}{space 2} .2781588{col 37}{space 1}    7.35{col 46}{space 3}0.000{col 54}{space 4} 1.500283{col 67}{space 3} 2.590646
{txt}{space 9}17  {c |}{col 14}{res}{space 2} 1.462822{col 26}{space 2}  .276834{col 37}{space 1}    5.28{col 46}{space 3}0.000{col 54}{space 4} .9202375{col 67}{space 3} 2.005407
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2} .6403047{col 26}{space 2} .3058984{col 37}{space 1}    2.09{col 46}{space 3}0.036{col 54}{space 4} .0407549{col 67}{space 3} 1.239854
{txt}{space 9}20  {c |}{col 14}{res}{space 2} 1.605241{col 26}{space 2} .2719505{col 37}{space 1}    5.90{col 46}{space 3}0.000{col 54}{space 4} 1.072228{col 67}{space 3} 2.138254
{txt}{space 9}21  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}22  {c |}{col 14}{res}{space 2}  2.19145{col 26}{space 2} .2601811{col 37}{space 1}    8.42{col 46}{space 3}0.000{col 54}{space 4} 1.681505{col 67}{space 3} 2.701396
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .6350969{col 26}{space 2} .3015188{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0441309{col 67}{space 3} 1.226063
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .8282479{col 26}{space 2} .2994025{col 37}{space 1}    2.77{col 46}{space 3}0.006{col 54}{space 4} .2414297{col 67}{space 3} 1.415066
{txt}{space 9}25  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}26  {c |}{col 14}{res}{space 2} 1.786905{col 26}{space 2} .2878952{col 37}{space 1}    6.21{col 46}{space 3}0.000{col 54}{space 4} 1.222641{col 67}{space 3} 2.351169
{txt}{space 9}27  {c |}{col 14}{res}{space 2} 2.640154{col 26}{space 2} .2613666{col 37}{space 1}   10.10{col 46}{space 3}0.000{col 54}{space 4} 2.127885{col 67}{space 3} 3.152423
{txt}{space 9}28  {c |}{col 14}{res}{space 2} 2.254034{col 26}{space 2} .2623633{col 37}{space 1}    8.59{col 46}{space 3}0.000{col 54}{space 4} 1.739811{col 67}{space 3} 2.768256
{txt}{space 9}29  {c |}{col 14}{res}{space 2}  2.09262{col 26}{space 2} .2560919{col 37}{space 1}    8.17{col 46}{space 3}0.000{col 54}{space 4} 1.590689{col 67}{space 3} 2.594551
{txt}{space 9}30  {c |}{col 14}{res}{space 2} 1.776076{col 26}{space 2} .2768205{col 37}{space 1}    6.42{col 46}{space 3}0.000{col 54}{space 4} 1.233518{col 67}{space 3} 2.318634
{txt}{space 9}31  {c |}{col 14}{res}{space 2} 2.092057{col 26}{space 2} .2620026{col 37}{space 1}    7.98{col 46}{space 3}0.000{col 54}{space 4} 1.578541{col 67}{space 3} 2.605572
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-3.869802{col 26}{space 2} .4173105{col 37}{space 1}   -9.27{col 46}{space 3}0.000{col 54}{space 4}-4.687715{col 67}{space 3}-3.051888
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict green2logit2a
{txt}(option {bf:pr} assumed; Pr(green2a))
(11,197 missing values generated)

{com}. 
. *4. With quadratic terms
. 
. gen edu2=education*education
{txt}(314 missing values generated)

{com}. gen agea2=agea*agea
{txt}(142 missing values generated)

{com}. gen y2=y*y
{txt}(389 missing values generated)

{com}. gen polintr2= polintr*polintr
{txt}(160 missing values generated)

{com}. 
. logit PS2a education edu2 agea agea2 female bebe minority y y2 uemp3m i.source polintr polintr2 i.election

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-22941.827}  
Iteration 1:{space 3}log likelihood = {res:-22127.971}  
Iteration 2:{space 3}log likelihood = {res:-22104.631}  
Iteration 3:{space 3}log likelihood = {res:-22104.486}  
Iteration 4:{space 3}log likelihood = {res:-22104.486}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    40,569
{txt}{col 49}LR chi2({res}44{txt}){col 67}= {res}   1674.68
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-22104.486{txt}{col 49}Pseudo R2{col 67}= {res}    0.0365

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        PS2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2}-.1484925{col 26}{space 2} .0328074{col 37}{space 1}   -4.53{col 46}{space 3}0.000{col 54}{space 4}-.2127939{col 67}{space 3}-.0841911
{txt}{space 8}edu2 {c |}{col 14}{res}{space 2} .0071448{col 26}{space 2} .0071631{col 37}{space 1}    1.00{col 46}{space 3}0.319{col 54}{space 4}-.0068946{col 67}{space 3} .0211843
{txt}{space 8}agea {c |}{col 14}{res}{space 2}  .018705{col 26}{space 2} .0041709{col 37}{space 1}    4.48{col 46}{space 3}0.000{col 54}{space 4} .0105301{col 67}{space 3} .0268798
{txt}{space 7}agea2 {c |}{col 14}{res}{space 2}-.0001619{col 26}{space 2} .0000413{col 37}{space 1}   -3.92{col 46}{space 3}0.000{col 54}{space 4}-.0002429{col 67}{space 3} -.000081
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0550525{col 26}{space 2} .0238296{col 37}{space 1}    2.31{col 46}{space 3}0.021{col 54}{space 4} .0083474{col 67}{space 3} .1017576
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}  .026311{col 26}{space 2} .0270447{col 37}{space 1}    0.97{col 46}{space 3}0.331{col 54}{space 4}-.0266956{col 67}{space 3} .0793177
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.6000284{col 26}{space 2} .0672995{col 37}{space 1}   -8.92{col 46}{space 3}0.000{col 54}{space 4} -.731933{col 67}{space 3}-.4681238
{txt}{space 11}y {c |}{col 14}{res}{space 2} .2596241{col 26}{space 2} .0646386{col 37}{space 1}    4.02{col 46}{space 3}0.000{col 54}{space 4} .1329348{col 67}{space 3} .3863133
{txt}{space 10}y2 {c |}{col 14}{res}{space 2}-.0530074{col 26}{space 2} .0142061{col 37}{space 1}   -3.73{col 46}{space 3}0.000{col 54}{space 4}-.0808508{col 67}{space 3} -.025164
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.1287365{col 26}{space 2} .0277564{col 37}{space 1}   -4.64{col 46}{space 3}0.000{col 54}{space 4} -.183138{col 67}{space 3} -.074335
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5004569{col 26}{space 2} .0435547{col 37}{space 1}   11.49{col 46}{space 3}0.000{col 54}{space 4} .4150912{col 67}{space 3} .5858226
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5187868{col 26}{space 2} .0703674{col 37}{space 1}    7.37{col 46}{space 3}0.000{col 54}{space 4} .3808693{col 67}{space 3} .6567042
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .5194088{col 26}{space 2} .0518634{col 37}{space 1}   10.01{col 46}{space 3}0.000{col 54}{space 4} .4177585{col 67}{space 3} .6210591
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.0192018{col 26}{space 2} .0670449{col 37}{space 1}   -0.29{col 46}{space 3}0.775{col 54}{space 4}-.1506073{col 67}{space 3} .1122037
{txt}{space 4}polintr2 {c |}{col 14}{res}{space 2}-.0123905{col 26}{space 2} .0131143{col 37}{space 1}   -0.94{col 46}{space 3}0.345{col 54}{space 4} -.038094{col 67}{space 3} .0133131
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.2539593{col 26}{space 2} .1179703{col 37}{space 1}   -2.15{col 46}{space 3}0.031{col 54}{space 4}-.4851767{col 67}{space 3}-.0227418
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4937948{col 26}{space 2} .0959263{col 37}{space 1}   -5.15{col 46}{space 3}0.000{col 54}{space 4}-.6818068{col 67}{space 3}-.3057827
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5776934{col 26}{space 2} .0844609{col 37}{space 1}    6.84{col 46}{space 3}0.000{col 54}{space 4}  .412153{col 67}{space 3} .7432337
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .6650564{col 26}{space 2} .0838515{col 37}{space 1}    7.93{col 46}{space 3}0.000{col 54}{space 4} .5007104{col 67}{space 3} .8294023
{txt}{space 10}6  {c |}{col 14}{res}{space 2} -.402464{col 26}{space 2} .0925654{col 37}{space 1}   -4.35{col 46}{space 3}0.000{col 54}{space 4}-.5838889{col 67}{space 3}-.2210391
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-1.302982{col 26}{space 2} .1206482{col 37}{space 1}  -10.80{col 46}{space 3}0.000{col 54}{space 4}-1.539448{col 67}{space 3}-1.066516
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.4417719{col 26}{space 2} .1127503{col 37}{space 1}   -3.92{col 46}{space 3}0.000{col 54}{space 4}-.6627585{col 67}{space 3}-.2207854
{txt}{space 10}9  {c |}{col 14}{res}{space 2}  .127963{col 26}{space 2} .0891486{col 37}{space 1}    1.44{col 46}{space 3}0.151{col 54}{space 4}-.0467651{col 67}{space 3} .3026911
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0480431{col 26}{space 2} .0919718{col 37}{space 1}   -0.52{col 46}{space 3}0.601{col 54}{space 4}-.2283044{col 67}{space 3} .1322182
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.1543091{col 26}{space 2} .0838561{col 37}{space 1}   -1.84{col 46}{space 3}0.066{col 54}{space 4}-.3186642{col 67}{space 3} .0100459
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0867623{col 26}{space 2} .0896829{col 37}{space 1}    0.97{col 46}{space 3}0.333{col 54}{space 4}-.0890131{col 67}{space 3} .2625376
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3098911{col 26}{space 2} .0888737{col 37}{space 1}    3.49{col 46}{space 3}0.000{col 54}{space 4} .1357018{col 67}{space 3} .4840803
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.1537925{col 26}{space 2} .0927952{col 37}{space 1}   -1.66{col 46}{space 3}0.097{col 54}{space 4}-.3356677{col 67}{space 3} .0280827
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.5636978{col 26}{space 2} .1094259{col 37}{space 1}   -5.15{col 46}{space 3}0.000{col 54}{space 4}-.7781686{col 67}{space 3}-.3492271
{txt}{space 9}17  {c |}{col 14}{res}{space 2}  .360458{col 26}{space 2} .0867068{col 37}{space 1}    4.16{col 46}{space 3}0.000{col 54}{space 4} .1905159{col 67}{space 3} .5304002
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .0628949{col 26}{space 2} .0871509{col 37}{space 1}    0.72{col 46}{space 3}0.470{col 54}{space 4}-.1079177{col 67}{space 3} .2337076
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2594492{col 26}{space 2} .0868632{col 37}{space 1}    2.99{col 46}{space 3}0.003{col 54}{space 4} .0892005{col 67}{space 3}  .429698
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.0181163{col 26}{space 2}  .091042{col 37}{space 1}   -0.20{col 46}{space 3}0.842{col 54}{space 4}-.1965554{col 67}{space 3} .1603228
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.1780776{col 26}{space 2} .0949818{col 37}{space 1}   -1.87{col 46}{space 3}0.061{col 54}{space 4}-.3642384{col 67}{space 3} .0080832
{txt}{space 9}22  {c |}{col 14}{res}{space 2} -.608206{col 26}{space 2} .0936585{col 37}{space 1}   -6.49{col 46}{space 3}0.000{col 54}{space 4}-.7917733{col 67}{space 3}-.4246388
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.8522509{col 26}{space 2} .0979282{col 37}{space 1}   -8.70{col 46}{space 3}0.000{col 54}{space 4}-1.044187{col 67}{space 3} -.660315
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.2011275{col 26}{space 2} .0916271{col 37}{space 1}   -2.20{col 46}{space 3}0.028{col 54}{space 4}-.3807133{col 67}{space 3}-.0215418
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .0583297{col 26}{space 2}   .11293{col 37}{space 1}    0.52{col 46}{space 3}0.605{col 54}{space 4} -.163009{col 67}{space 3} .2796685
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.2258412{col 26}{space 2} .0957042{col 37}{space 1}   -2.36{col 46}{space 3}0.018{col 54}{space 4} -.413418{col 67}{space 3}-.0382643
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .0497173{col 26}{space 2} .0925198{col 37}{space 1}    0.54{col 46}{space 3}0.591{col 54}{space 4}-.1316182{col 67}{space 3} .2310529
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.5446875{col 26}{space 2} .0986184{col 37}{space 1}   -5.52{col 46}{space 3}0.000{col 54}{space 4}-.7379759{col 67}{space 3}-.3513991
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.0565428{col 26}{space 2} .0819009{col 37}{space 1}   -0.69{col 46}{space 3}0.490{col 54}{space 4}-.2170655{col 67}{space 3} .1039799
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1091355{col 26}{space 2}  .093974{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}  -.07505{col 67}{space 3} .2933211
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .0949108{col 26}{space 2}  .088077{col 37}{space 1}    1.08{col 46}{space 3}0.281{col 54}{space 4} -.077717{col 67}{space 3} .2675386
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} -.553854{col 26}{space 2} .2288546{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-1.002401{col 67}{space 3}-.1053073
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict PS2logit3a
{txt}(option {bf:pr} assumed; Pr(PS2a))
(2,083 missing values generated)

{com}. 
. logit left2a education edu2 agea agea2 female bebe minority y y2 uemp3m i.source polintr polintr2 i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1497 obs not used

note: 7.election != 0 predicts failure perfectly
      7.election dropped and 1197 obs not used

note: 9.election != 0 predicts failure perfectly
      9.election dropped and 1369 obs not used

note: 15.election != 0 predicts failure perfectly
      15.election dropped and 1309 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1475 obs not used

note: 23.election != 0 predicts failure perfectly
      23.election dropped and 1658 obs not used

note: 27.election != 0 predicts failure perfectly
      27.election dropped and 1178 obs not used

note: 31.election != 0 predicts failure perfectly
      31.election dropped and 1399 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-7971.1135}  
Iteration 1:{space 3}log likelihood = {res:-7518.8566}  
Iteration 2:{space 3}log likelihood = {res:-7398.8333}  
Iteration 3:{space 3}log likelihood = {res:-7396.7954}  
Iteration 4:{space 3}log likelihood = {res:-7396.7651}  
Iteration 5:{space 3}log likelihood = {res: -7396.765}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,487
{txt}{col 49}LR chi2({res}36{txt}){col 67}= {res}   1148.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -7396.765{txt}{col 49}Pseudo R2{col 67}= {res}    0.0721

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      left2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .0882701{col 26}{space 2} .0704957{col 37}{space 1}    1.25{col 46}{space 3}0.211{col 54}{space 4} -.049899{col 67}{space 3} .2264393
{txt}{space 8}edu2 {c |}{col 14}{res}{space 2}-.0075871{col 26}{space 2} .0144785{col 37}{space 1}   -0.52{col 46}{space 3}0.600{col 54}{space 4}-.0359644{col 67}{space 3} .0207902
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0099621{col 26}{space 2} .0086412{col 37}{space 1}    1.15{col 46}{space 3}0.249{col 54}{space 4}-.0069742{col 67}{space 3} .0268985
{txt}{space 7}agea2 {c |}{col 14}{res}{space 2}-.0001932{col 26}{space 2} .0000909{col 37}{space 1}   -2.13{col 46}{space 3}0.034{col 54}{space 4}-.0003714{col 67}{space 3}-.0000151
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0018293{col 26}{space 2} .0457535{col 37}{space 1}    0.04{col 46}{space 3}0.968{col 54}{space 4}-.0878459{col 67}{space 3} .0915045
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .2139587{col 26}{space 2} .0515751{col 37}{space 1}    4.15{col 46}{space 3}0.000{col 54}{space 4} .1128734{col 67}{space 3} .3150441
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0413199{col 26}{space 2} .1361832{col 37}{space 1}   -0.30{col 46}{space 3}0.762{col 54}{space 4}-.3082342{col 67}{space 3} .2255943
{txt}{space 11}y {c |}{col 14}{res}{space 2}  .757798{col 26}{space 2} .1242279{col 37}{space 1}    6.10{col 46}{space 3}0.000{col 54}{space 4} .5143159{col 67}{space 3}  1.00128
{txt}{space 10}y2 {c |}{col 14}{res}{space 2}-.1244601{col 26}{space 2} .0267847{col 37}{space 1}   -4.65{col 46}{space 3}0.000{col 54}{space 4}-.1769571{col 67}{space 3} -.071963
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.3621555{col 26}{space 2} .0494842{col 37}{space 1}   -7.32{col 46}{space 3}0.000{col 54}{space 4}-.4591428{col 67}{space 3}-.2651682
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3618363{col 26}{space 2} .0802667{col 37}{space 1}    4.51{col 46}{space 3}0.000{col 54}{space 4} .2045165{col 67}{space 3} .5191562
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6554198{col 26}{space 2}  .122392{col 37}{space 1}    5.36{col 46}{space 3}0.000{col 54}{space 4} .4155359{col 67}{space 3} .8953037
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2306926{col 26}{space 2} .1024576{col 37}{space 1}    2.25{col 46}{space 3}0.024{col 54}{space 4} .0298795{col 67}{space 3} .4315057
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.4164053{col 26}{space 2} .1246399{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4} -.660695{col 67}{space 3}-.1721156
{txt}{space 4}polintr2 {c |}{col 14}{res}{space 2} .0367648{col 26}{space 2} .0252793{col 37}{space 1}    1.45{col 46}{space 3}0.146{col 54}{space 4}-.0127817{col 67}{space 3} .0863114
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-2.222349{col 26}{space 2}  .347508{col 37}{space 1}   -6.40{col 46}{space 3}0.000{col 54}{space 4}-2.903452{col 67}{space 3}-1.541246
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4939477{col 26}{space 2} .1275431{col 37}{space 1}   -3.87{col 46}{space 3}0.000{col 54}{space 4}-.7439276{col 67}{space 3}-.2439677
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2} -1.45971{col 26}{space 2} .1640515{col 37}{space 1}   -8.90{col 46}{space 3}0.000{col 54}{space 4}-1.781245{col 67}{space 3}-1.138175
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-1.041744{col 26}{space 2} .1419368{col 37}{space 1}   -7.34{col 46}{space 3}0.000{col 54}{space 4}-1.319935{col 67}{space 3}-.7635528
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}8  {c |}{col 14}{res}{space 2}-1.126709{col 26}{space 2} .1899832{col 37}{space 1}   -5.93{col 46}{space 3}0.000{col 54}{space 4}-1.499069{col 67}{space 3}-.7543485
{txt}{space 10}9  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}10  {c |}{col 14}{res}{space 2}-1.496863{col 26}{space 2} .1843739{col 37}{space 1}   -8.12{col 46}{space 3}0.000{col 54}{space 4}-1.858229{col 67}{space 3}-1.135497
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.5658499{col 26}{space 2} .1149148{col 37}{space 1}   -4.92{col 46}{space 3}0.000{col 54}{space 4}-.7910787{col 67}{space 3} -.340621
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-1.233995{col 26}{space 2} .1568517{col 37}{space 1}   -7.87{col 46}{space 3}0.000{col 54}{space 4}-1.541419{col 67}{space 3}-.9265712
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.1068417{col 26}{space 2} .1227814{col 37}{space 1}   -0.87{col 46}{space 3}0.384{col 54}{space 4}-.3474889{col 67}{space 3} .1338055
{txt}{space 9}15  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}16  {c |}{col 14}{res}{space 2}-2.898863{col 26}{space 2} .3891695{col 37}{space 1}   -7.45{col 46}{space 3}0.000{col 54}{space 4}-3.661621{col 67}{space 3}-2.136104
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-1.118849{col 26}{space 2}  .153097{col 37}{space 1}   -7.31{col 46}{space 3}0.000{col 54}{space 4}-1.418914{col 67}{space 3}-.8187847
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2}-.4901045{col 26}{space 2} .1283232{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.7416133{col 67}{space 3}-.2385957
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.3669706{col 26}{space 2}  .125145{col 37}{space 1}   -2.93{col 46}{space 3}0.003{col 54}{space 4}-.6122503{col 67}{space 3}-.1216909
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.8155141{col 26}{space 2} .1448145{col 37}{space 1}   -5.63{col 46}{space 3}0.000{col 54}{space 4}-1.099345{col 67}{space 3}-.5316828
{txt}{space 9}22  {c |}{col 14}{res}{space 2} -1.09051{col 26}{space 2} .1383369{col 37}{space 1}   -7.88{col 46}{space 3}0.000{col 54}{space 4}-1.361645{col 67}{space 3}-.8193749
{txt}{space 9}23  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}24  {c |}{col 14}{res}{space 2}-.4979407{col 26}{space 2} .1275374{col 37}{space 1}   -3.90{col 46}{space 3}0.000{col 54}{space 4}-.7479094{col 67}{space 3} -.247972
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-1.251673{col 26}{space 2} .2074239{col 37}{space 1}   -6.03{col 46}{space 3}0.000{col 54}{space 4}-1.658217{col 67}{space 3}-.8451299
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-1.300642{col 26}{space 2}  .173515{col 37}{space 1}   -7.50{col 46}{space 3}0.000{col 54}{space 4}-1.640725{col 67}{space 3}-.9605591
{txt}{space 9}27  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}28  {c |}{col 14}{res}{space 2} .4950947{col 26}{space 2} .1095197{col 37}{space 1}    4.52{col 46}{space 3}0.000{col 54}{space 4} .2804401{col 67}{space 3} .7097493
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.3193595{col 26}{space 2} .1083565{col 37}{space 1}   -2.95{col 46}{space 3}0.003{col 54}{space 4}-.5317343{col 67}{space 3}-.1069847
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-1.242759{col 26}{space 2} .1685104{col 37}{space 1}   -7.37{col 46}{space 3}0.000{col 54}{space 4}-1.573033{col 67}{space 3}-.9124845
{txt}{space 9}31  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-2.054701{col 26}{space 2} .4347702{col 37}{space 1}   -4.73{col 46}{space 3}0.000{col 54}{space 4}-2.906835{col 67}{space 3}-1.202567
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict left2logit3a
{txt}(option {bf:pr} assumed; Pr(left2a))
(17,177 missing values generated)

{com}. 
. logit green2a education edu2 agea agea2 female bebe minority y y2 uemp3m i.source polintr polintr2 i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1497 obs not used

note: 5.election != 0 predicts failure perfectly
      5.election dropped and 1658 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1475 obs not used

note: 21.election != 0 predicts failure perfectly
      21.election dropped and 1227 obs not used

note: 25.election != 0 predicts failure perfectly
      25.election dropped and 601 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-8325.5737}  
Iteration 1:{space 3}log likelihood = {res: -7667.552}  
Iteration 2:{space 3}log likelihood = {res:-7523.9748}  
Iteration 3:{space 3}log likelihood = {res:-7521.3069}  
Iteration 4:{space 3}log likelihood = {res:-7521.2989}  
Iteration 5:{space 3}log likelihood = {res:-7521.2989}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    34,111
{txt}{col 49}LR chi2({res}39{txt}){col 67}= {res}   1608.55
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7521.2989{txt}{col 49}Pseudo R2{col 67}= {res}    0.0966

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     green2a{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .2890721{col 26}{space 2} .0876627{col 37}{space 1}    3.30{col 46}{space 3}0.001{col 54}{space 4} .1172563{col 67}{space 3}  .460888
{txt}{space 8}edu2 {c |}{col 14}{res}{space 2} .0072341{col 26}{space 2} .0168758{col 37}{space 1}    0.43{col 46}{space 3}0.668{col 54}{space 4} -.025842{col 67}{space 3} .0403102
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0069854{col 26}{space 2} .0091933{col 37}{space 1}    0.76{col 46}{space 3}0.447{col 54}{space 4}-.0110331{col 67}{space 3} .0250038
{txt}{space 7}agea2 {c |}{col 14}{res}{space 2}-.0002649{col 26}{space 2} .0000996{col 37}{space 1}   -2.66{col 46}{space 3}0.008{col 54}{space 4}  -.00046{col 67}{space 3}-.0000697
{txt}{space 6}female {c |}{col 14}{res}{space 2} .2497945{col 26}{space 2} .0459028{col 37}{space 1}    5.44{col 46}{space 3}0.000{col 54}{space 4} .1598266{col 67}{space 3} .3397624
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0936116{col 26}{space 2} .0510711{col 37}{space 1}    1.83{col 46}{space 3}0.067{col 54}{space 4} -.006486{col 67}{space 3} .1937092
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0758727{col 26}{space 2} .1360641{col 37}{space 1}   -0.56{col 46}{space 3}0.577{col 54}{space 4}-.3425535{col 67}{space 3} .1908081
{txt}{space 11}y {c |}{col 14}{res}{space 2} -.070267{col 26}{space 2} .1278513{col 37}{space 1}   -0.55{col 46}{space 3}0.583{col 54}{space 4}-.3208509{col 67}{space 3} .1803169
{txt}{space 10}y2 {c |}{col 14}{res}{space 2} .0203874{col 26}{space 2} .0295351{col 37}{space 1}    0.69{col 46}{space 3}0.490{col 54}{space 4}-.0375003{col 67}{space 3}  .078275
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.2093221{col 26}{space 2} .0515335{col 37}{space 1}   -4.06{col 46}{space 3}0.000{col 54}{space 4}-.3103259{col 67}{space 3}-.1083183
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1517791{col 26}{space 2}  .072411{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-.2937021{col 67}{space 3}-.0098561
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1505688{col 26}{space 2} .1216081{col 37}{space 1}    1.24{col 46}{space 3}0.216{col 54}{space 4}-.0877787{col 67}{space 3} .3889162
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0767267{col 26}{space 2} .1019023{col 37}{space 1}   -0.75{col 46}{space 3}0.451{col 54}{space 4}-.2764514{col 67}{space 3} .1229981
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.2765536{col 26}{space 2} .1286838{col 37}{space 1}   -2.15{col 46}{space 3}0.032{col 54}{space 4}-.5287691{col 67}{space 3} -.024338
{txt}{space 4}polintr2 {c |}{col 14}{res}{space 2} .0159093{col 26}{space 2} .0268842{col 37}{space 1}    0.59{col 46}{space 3}0.554{col 54}{space 4}-.0367828{col 67}{space 3} .0686014
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.703987{col 26}{space 2} .3053729{col 37}{space 1}    5.58{col 46}{space 3}0.000{col 54}{space 4} 1.105467{col 67}{space 3} 2.302507
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.968113{col 26}{space 2} .2665277{col 37}{space 1}    7.38{col 46}{space 3}0.000{col 54}{space 4} 1.445729{col 67}{space 3} 2.490498
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}6  {c |}{col 14}{res}{space 2} 2.065617{col 26}{space 2} .2637838{col 37}{space 1}    7.83{col 46}{space 3}0.000{col 54}{space 4}  1.54861{col 67}{space 3} 2.582623
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .6895691{col 26}{space 2}   .30824{col 37}{space 1}    2.24{col 46}{space 3}0.025{col 54}{space 4} .0854297{col 67}{space 3} 1.293708
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .6038356{col 26}{space 2} .3664217{col 37}{space 1}    1.65{col 46}{space 3}0.099{col 54}{space 4}-.1143377{col 67}{space 3} 1.322009
{txt}{space 10}9  {c |}{col 14}{res}{space 2} 2.549254{col 26}{space 2} .2597011{col 37}{space 1}    9.82{col 46}{space 3}0.000{col 54}{space 4}  2.04025{col 67}{space 3} 3.058259
{txt}{space 9}10  {c |}{col 14}{res}{space 2} 1.598589{col 26}{space 2}  .295411{col 37}{space 1}    5.41{col 46}{space 3}0.000{col 54}{space 4} 1.019594{col 67}{space 3} 2.177584
{txt}{space 9}11  {c |}{col 14}{res}{space 2} 2.467247{col 26}{space 2} .2549269{col 37}{space 1}    9.68{col 46}{space 3}0.000{col 54}{space 4} 1.967599{col 67}{space 3} 2.966894
{txt}{space 9}12  {c |}{col 14}{res}{space 2} 1.421616{col 26}{space 2} .2792885{col 37}{space 1}    5.09{col 46}{space 3}0.000{col 54}{space 4} .8742204{col 67}{space 3} 1.969011
{txt}{space 9}13  {c |}{col 14}{res}{space 2}  1.19866{col 26}{space 2} .2871891{col 37}{space 1}    4.17{col 46}{space 3}0.000{col 54}{space 4} .6357796{col 67}{space 3}  1.76154
{txt}{space 9}15  {c |}{col 14}{res}{space 2} 2.713306{col 26}{space 2} .2583283{col 37}{space 1}   10.50{col 46}{space 3}0.000{col 54}{space 4} 2.206992{col 67}{space 3}  3.21962
{txt}{space 9}16  {c |}{col 14}{res}{space 2} 2.033919{col 26}{space 2} .2782176{col 37}{space 1}    7.31{col 46}{space 3}0.000{col 54}{space 4} 1.488623{col 67}{space 3} 2.579216
{txt}{space 9}17  {c |}{col 14}{res}{space 2}  1.47037{col 26}{space 2} .2768696{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4} .9277153{col 67}{space 3} 2.013024
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2} .6209665{col 26}{space 2} .3063311{col 37}{space 1}    2.03{col 46}{space 3}0.043{col 54}{space 4} .0205685{col 67}{space 3} 1.221364
{txt}{space 9}20  {c |}{col 14}{res}{space 2} 1.613409{col 26}{space 2} .2719935{col 37}{space 1}    5.93{col 46}{space 3}0.000{col 54}{space 4} 1.080312{col 67}{space 3} 2.146507
{txt}{space 9}21  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}22  {c |}{col 14}{res}{space 2} 2.203982{col 26}{space 2} .2605236{col 37}{space 1}    8.46{col 46}{space 3}0.000{col 54}{space 4} 1.693365{col 67}{space 3} 2.714598
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .6374981{col 26}{space 2} .3016161{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0463414{col 67}{space 3} 1.228655
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .8336205{col 26}{space 2} .2994697{col 37}{space 1}    2.78{col 46}{space 3}0.005{col 54}{space 4} .2466706{col 67}{space 3}  1.42057
{txt}{space 9}25  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}26  {c |}{col 14}{res}{space 2} 1.772241{col 26}{space 2} .2887766{col 37}{space 1}    6.14{col 46}{space 3}0.000{col 54}{space 4} 1.206249{col 67}{space 3} 2.338233
{txt}{space 9}27  {c |}{col 14}{res}{space 2} 2.649073{col 26}{space 2} .2617921{col 37}{space 1}   10.12{col 46}{space 3}0.000{col 54}{space 4}  2.13597{col 67}{space 3} 3.162176
{txt}{space 9}28  {c |}{col 14}{res}{space 2} 2.265227{col 26}{space 2} .2624435{col 37}{space 1}    8.63{col 46}{space 3}0.000{col 54}{space 4} 1.750848{col 67}{space 3} 2.779607
{txt}{space 9}29  {c |}{col 14}{res}{space 2} 2.104773{col 26}{space 2} .2563888{col 37}{space 1}    8.21{col 46}{space 3}0.000{col 54}{space 4}  1.60226{col 67}{space 3} 2.607285
{txt}{space 9}30  {c |}{col 14}{res}{space 2} 1.782863{col 26}{space 2} .2769616{col 37}{space 1}    6.44{col 46}{space 3}0.000{col 54}{space 4} 1.240028{col 67}{space 3} 2.325697
{txt}{space 9}31  {c |}{col 14}{res}{space 2} 2.107955{col 26}{space 2} .2621345{col 37}{space 1}    8.04{col 46}{space 3}0.000{col 54}{space 4} 1.594181{col 67}{space 3} 2.621729
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-4.262285{col 26}{space 2} .4992564{col 37}{space 1}   -8.54{col 46}{space 3}0.000{col 54}{space 4}-5.240809{col 67}{space 3} -3.28376
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict green2logit3a
{txt}(option {bf:pr} assumed; Pr(green2a))
(11,197 missing values generated)

{com}. 
. 
. *Create variables to ease the reading of the table
. 
. gen real_PSa= PS if voting2a==0
{txt}(53,037 missing values generated)

{com}. gen CEM_PSa= PS2a if cem_matched==1 
{txt}(50,148 missing values generated)

{com}. gen logit_PSa = PS2logita if inclusiona==1  
{txt}(53,126 missing values generated)

{com}. gen logitplus_PSa = PS2logit2a if inclusiona==1  & likely_abstainera==0
{txt}(53,126 missing values generated)

{com}. gen quadratic_PSa =  PS2logit3a if inclusiona==1
{txt}(53,126 missing values generated)

{com}. 
. *Extreme left parties
. 
. gen real_lefta= left if voting2a==0
{txt}(53,037 missing values generated)

{com}. gen CEM_lefta= left2a if cem_matched==1 
{txt}(50,148 missing values generated)

{com}. gen logit_lefta = left2logita if inclusiona==1  
{txt}(53,925 missing values generated)

{com}. gen logitplus_lefta = left2logit2a if inclusiona==1  & likely_abstainera==0
{txt}(53,925 missing values generated)

{com}. gen quadratic_lefta =  left2logit3a if inclusiona==1
{txt}(53,925 missing values generated)

{com}. 
. *Green parties
. 
. gen real_greena= green if voting2a==0
{txt}(53,037 missing values generated)

{com}. gen CEM_greena= green2a if cem_matched==1 
{txt}(50,148 missing values generated)

{com}. gen logit_greena = green2logita if inclusiona==1  
{txt}(53,576 missing values generated)

{com}. gen logitplus_greena = green2logit2a if inclusiona==1  & likely_abstainera==0
{txt}(53,576 missing values generated)

{com}. gen quadratic_greena =  green2logit3a if inclusiona==1
{txt}(53,576 missing values generated)

{com}. 
. 
. * First step: we create a variable that is a random number, but only for the people who vote
. 
. gen random=runiform(0,1) if voting==1 & education<4 & y>2  
{txt}(48,378 missing values generated)

{com}. 
. * Second step: we use this random number to pick 3000 voters at random among all voters
. * This variable is called "inclusion" (0=NOT in the sample 3000 voters, 1=in sample the 3000 voters)
. 
. sort random
{txt}
{com}. gen inclusion=0 if voting==1
{txt}(11,137 missing values generated)

{com}. replace inclusion=1 if _n<3001 & voting==1
{txt}(3,000 real changes made)

{com}. 
. * Third step: we create alternative vote variables. It's the same than the original one
. * Except for people in the sample of 3000 voters. For them we remove the vote variables, and put missing values
. 
. gen voting2=voting
{txt}
{com}. replace voting2=0 if inclusion==1
{txt}(3,000 real changes made)

{com}. 
. gen PS2=PS
{txt}(11,137 missing values generated)

{com}. replace PS2=. if inclusion==1
{txt}(3,000 real changes made, 3,000 to missing)

{com}. 
. gen left2=left
{txt}(11,137 missing values generated)

{com}. replace left2=. if inclusion==1
{txt}(3,000 real changes made, 3,000 to missing)

{com}. 
. gen green2=green
{txt}(11,137 missing values generated)

{com}. replace green2=. if inclusion==1
{txt}(3,000 real changes made, 3,000 to missing)

{com}. 
. * Fourth step: let's now see whether we can recover the vote variable in the sample of 3000 voters.
. 
. * 1. With a normal CEM (we exclude real abstainers)
. 
. cem education agea (24 34 44 54 64) female bebe minority y uemp3m source(#0) polintr election(#0) if voting==1, treatment(voting2)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}26534
{txt}Number of matched strata: {res}503

           {txt}    0      1
      All  {res} 3000  41900
{txt}  Matched  {res}  875   1068
{txt}Unmatched  {res} 2125  40832


{txt}Multivariate L1 distance: {res}.53575291

{txt}Univariate imbalance:

                 L1      mean       min       25%       50%       75%       max
education  {res} 1.5e-15  -1.9e-15         0         0         0         0         0
{txt}     agea  {res}  .05858   -.08449        -1         0         0        -1        -3
{txt}   female  {res} 1.9e-15  -2.0e-15         0         0         0         0         0
{txt}     bebe  {res} 1.8e-15  -2.2e-15         0         0         0         0         0
{txt} minority  {res} 5.8e-17         0         0         0         0         0         0
{txt}        y  {res} 1.3e-15   7.5e-15         0         0         0         0         .
{txt}   uemp3m  {res} 1.7e-15  -2.0e-15         0         0         0         0         0
{txt}   source  {res} 1.6e-15   6.7e-15         0         0         0         0         0
{txt}  polintr  {res} 1.4e-15   5.3e-15         0         0         0         0         0
{txt} election  {res} 1.1e-15  -9.2e-14         0         0         0         0         0
{txt}
{com}. 
. 
. * We compare the vote PS in the sample of 3000 vooters, in reality and with CEM
. 
. 
. * 2. With normal logit
. 
. logit PS2 education agea female bebe minority y uemp3m i.source polintr i.election

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-22842.308}  
Iteration 1:{space 3}log likelihood = {res: -22034.46}  
Iteration 2:{space 3}log likelihood = {res:-22010.618}  
Iteration 3:{space 3}log likelihood = {res:-22010.474}  
Iteration 4:{space 3}log likelihood = {res:-22010.474}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    40,640
{txt}{col 49}LR chi2({res}40{txt}){col 67}= {res}   1663.67
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-22010.474{txt}{col 49}Pseudo R2{col 67}= {res}    0.0364

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         PS2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2}-.1117043{col 26}{space 2} .0100225{col 37}{space 1}  -11.15{col 46}{space 3}0.000{col 54}{space 4}-.1313479{col 67}{space 3}-.0920606
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0027668{col 26}{space 2} .0009757{col 37}{space 1}    2.84{col 46}{space 3}0.005{col 54}{space 4} .0008544{col 67}{space 3} .0046793
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0510744{col 26}{space 2} .0238732{col 37}{space 1}    2.14{col 46}{space 3}0.032{col 54}{space 4} .0042837{col 67}{space 3} .0978651
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}-.0065644{col 26}{space 2} .0263683{col 37}{space 1}   -0.25{col 46}{space 3}0.803{col 54}{space 4}-.0582452{col 67}{space 3} .0451165
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.5895711{col 26}{space 2} .0690332{col 37}{space 1}   -8.54{col 46}{space 3}0.000{col 54}{space 4}-.7248737{col 67}{space 3}-.4542686
{txt}{space 11}y {c |}{col 14}{res}{space 2} .0448464{col 26}{space 2} .0179048{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} .0097536{col 67}{space 3} .0799391
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.1507291{col 26}{space 2} .0277473{col 37}{space 1}   -5.43{col 46}{space 3}0.000{col 54}{space 4}-.2051129{col 67}{space 3}-.0963454
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5407039{col 26}{space 2} .0441112{col 37}{space 1}   12.26{col 46}{space 3}0.000{col 54}{space 4} .4542475{col 67}{space 3} .6271603
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5270906{col 26}{space 2} .0737908{col 37}{space 1}    7.14{col 46}{space 3}0.000{col 54}{space 4} .3824633{col 67}{space 3} .6717178
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .5362851{col 26}{space 2} .0516041{col 37}{space 1}   10.39{col 46}{space 3}0.000{col 54}{space 4} .4351428{col 67}{space 3} .6374273
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.0925665{col 26}{space 2} .0145156{col 37}{space 1}   -6.38{col 46}{space 3}0.000{col 54}{space 4}-.1210165{col 67}{space 3}-.0641165
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.2677548{col 26}{space 2} .1155398{col 37}{space 1}   -2.32{col 46}{space 3}0.020{col 54}{space 4}-.4942087{col 67}{space 3}-.0413009
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4917808{col 26}{space 2} .0938608{col 37}{space 1}   -5.24{col 46}{space 3}0.000{col 54}{space 4}-.6757447{col 67}{space 3}-.3078169
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5810755{col 26}{space 2}  .081565{col 37}{space 1}    7.12{col 46}{space 3}0.000{col 54}{space 4}  .421211{col 67}{space 3}   .74094
{txt}{space 10}5  {c |}{col 14}{res}{space 2}  .656419{col 26}{space 2} .0815642{col 37}{space 1}    8.05{col 46}{space 3}0.000{col 54}{space 4} .4965561{col 67}{space 3}  .816282
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.3768953{col 26}{space 2} .0902028{col 37}{space 1}   -4.18{col 46}{space 3}0.000{col 54}{space 4}-.5536896{col 67}{space 3} -.200101
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-1.325396{col 26}{space 2} .1202246{col 37}{space 1}  -11.02{col 46}{space 3}0.000{col 54}{space 4}-1.561032{col 67}{space 3} -1.08976
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.3972884{col 26}{space 2} .1105671{col 37}{space 1}   -3.59{col 46}{space 3}0.000{col 54}{space 4} -.613996{col 67}{space 3}-.1805808
{txt}{space 10}9  {c |}{col 14}{res}{space 2}  .157289{col 26}{space 2} .0868333{col 37}{space 1}    1.81{col 46}{space 3}0.070{col 54}{space 4}-.0129011{col 67}{space 3} .3274792
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0122116{col 26}{space 2} .0910394{col 37}{space 1}    0.13{col 46}{space 3}0.893{col 54}{space 4}-.1662223{col 67}{space 3} .1906455
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.1440902{col 26}{space 2}  .081655{col 37}{space 1}   -1.76{col 46}{space 3}0.078{col 54}{space 4} -.304131{col 67}{space 3} .0159507
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1510478{col 26}{space 2} .0866988{col 37}{space 1}    1.74{col 46}{space 3}0.081{col 54}{space 4}-.0188788{col 67}{space 3} .3209744
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3258108{col 26}{space 2} .0892904{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .1508048{col 67}{space 3} .5008168
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.2062415{col 26}{space 2}  .092028{col 37}{space 1}   -2.24{col 46}{space 3}0.025{col 54}{space 4}-.3866131{col 67}{space 3}  -.02587
{txt}{space 9}16  {c |}{col 14}{res}{space 2} -.562274{col 26}{space 2} .1072019{col 37}{space 1}   -5.24{col 46}{space 3}0.000{col 54}{space 4} -.772386{col 67}{space 3}-.3521621
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3612612{col 26}{space 2} .0839368{col 37}{space 1}    4.30{col 46}{space 3}0.000{col 54}{space 4} .1967482{col 67}{space 3} .5257743
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .0674116{col 26}{space 2} .0853143{col 37}{space 1}    0.79{col 46}{space 3}0.429{col 54}{space 4}-.0998014{col 67}{space 3} .2346245
{txt}{space 9}19  {c |}{col 14}{res}{space 2}  .279539{col 26}{space 2} .0881002{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4} .1068659{col 67}{space 3} .4522122
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .0016341{col 26}{space 2} .0878776{col 37}{space 1}    0.02{col 46}{space 3}0.985{col 54}{space 4}-.1706028{col 67}{space 3} .1738709
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.1353385{col 26}{space 2} .0942297{col 37}{space 1}   -1.44{col 46}{space 3}0.151{col 54}{space 4}-.3200254{col 67}{space 3} .0493484
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.5925892{col 26}{space 2} .0915157{col 37}{space 1}   -6.48{col 46}{space 3}0.000{col 54}{space 4}-.7719566{col 67}{space 3}-.4132218
{txt}{space 9}23  {c |}{col 14}{res}{space 2} -.819005{col 26}{space 2} .0973223{col 37}{space 1}   -8.42{col 46}{space 3}0.000{col 54}{space 4}-1.009753{col 67}{space 3}-.6282568
{txt}{space 9}24  {c |}{col 14}{res}{space 2} -.179064{col 26}{space 2} .0890704{col 37}{space 1}   -2.01{col 46}{space 3}0.044{col 54}{space 4}-.3536387{col 67}{space 3}-.0044892
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .1407856{col 26}{space 2} .1117483{col 37}{space 1}    1.26{col 46}{space 3}0.208{col 54}{space 4}-.0782369{col 67}{space 3} .3598081
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.2412982{col 26}{space 2} .0975064{col 37}{space 1}   -2.47{col 46}{space 3}0.013{col 54}{space 4}-.4324073{col 67}{space 3}-.0501891
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .0840776{col 26}{space 2} .0898824{col 37}{space 1}    0.94{col 46}{space 3}0.350{col 54}{space 4}-.0920886{col 67}{space 3} .2602439
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.5217018{col 26}{space 2} .0972119{col 37}{space 1}   -5.37{col 46}{space 3}0.000{col 54}{space 4}-.7122335{col 67}{space 3}  -.33117
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.0715508{col 26}{space 2} .0795703{col 37}{space 1}   -0.90{col 46}{space 3}0.369{col 54}{space 4}-.2275057{col 67}{space 3} .0844042
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1441826{col 26}{space 2} .0918072{col 37}{space 1}    1.57{col 46}{space 3}0.116{col 54}{space 4}-.0357562{col 67}{space 3} .3241214
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .0632664{col 26}{space 2} .0859022{col 37}{space 1}    0.74{col 46}{space 3}0.461{col 54}{space 4}-.1050988{col 67}{space 3} .2316316
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0650419{col 26}{space 2}  .184773{col 37}{space 1}    0.35{col 46}{space 3}0.725{col 54}{space 4}-.2971066{col 67}{space 3} .4271904
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict PS2logit
{txt}(option {bf:pr} assumed; Pr(PS2))
(2,083 missing values generated)

{com}. 
. logit left2 education agea female bebe minority   y uemp3m i.source  polintr    i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1561 obs not used

note: 7.election != 0 predicts failure perfectly
      7.election dropped and 1221 obs not used

note: 9.election != 0 predicts failure perfectly
      9.election dropped and 1390 obs not used

note: 15.election != 0 predicts failure perfectly
      15.election dropped and 1319 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1490 obs not used

note: 23.election != 0 predicts failure perfectly
      23.election dropped and 1594 obs not used

note: 27.election != 0 predicts failure perfectly
      27.election dropped and 1212 obs not used

note: 31.election != 0 predicts failure perfectly
      31.election dropped and 1455 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -7954.062}  
Iteration 1:{space 3}log likelihood = {res:-7512.2546}  
Iteration 2:{space 3}log likelihood = {res:-7418.4165}  
Iteration 3:{space 3}log likelihood = {res:-7416.2585}  
Iteration 4:{space 3}log likelihood = {res:-7416.2284}  
Iteration 5:{space 3}log likelihood = {res:-7416.2284}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,398
{txt}{col 49}LR chi2({res}32{txt}){col 67}= {res}   1075.67
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7416.2284{txt}{col 49}Pseudo R2{col 67}= {res}    0.0676

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       left2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .0506874{col 26}{space 2} .0194505{col 37}{space 1}    2.61{col 46}{space 3}0.009{col 54}{space 4} .0125651{col 67}{space 3} .0888096
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0072177{col 26}{space 2} .0018215{col 37}{space 1}   -3.96{col 46}{space 3}0.000{col 54}{space 4}-.0107878{col 67}{space 3}-.0036476
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0316884{col 26}{space 2} .0456665{col 37}{space 1}    0.69{col 46}{space 3}0.488{col 54}{space 4}-.0578164{col 67}{space 3} .1211931
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .1797403{col 26}{space 2} .0494126{col 37}{space 1}    3.64{col 46}{space 3}0.000{col 54}{space 4} .0828933{col 67}{space 3} .2765872
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0775671{col 26}{space 2} .1399509{col 37}{space 1}   -0.55{col 46}{space 3}0.579{col 54}{space 4}-.3518658{col 67}{space 3} .1967317
{txt}{space 11}y {c |}{col 14}{res}{space 2} .2216702{col 26}{space 2} .0331974{col 37}{space 1}    6.68{col 46}{space 3}0.000{col 54}{space 4} .1566045{col 67}{space 3} .2867359
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.3713122{col 26}{space 2} .0489715{col 37}{space 1}   -7.58{col 46}{space 3}0.000{col 54}{space 4}-.4672945{col 67}{space 3}-.2753298
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  .391925{col 26}{space 2} .0805062{col 37}{space 1}    4.87{col 46}{space 3}0.000{col 54}{space 4} .2341357{col 67}{space 3} .5497143
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6333135{col 26}{space 2} .1270534{col 37}{space 1}    4.98{col 46}{space 3}0.000{col 54}{space 4} .3842934{col 67}{space 3} .8823336
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1606875{col 26}{space 2} .0997398{col 37}{space 1}    1.61{col 46}{space 3}0.107{col 54}{space 4} -.034799{col 67}{space 3}  .356174
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.2447149{col 26}{space 2} .0288591{col 37}{space 1}   -8.48{col 46}{space 3}0.000{col 54}{space 4}-.3012777{col 67}{space 3}-.1881522
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-2.277286{col 26}{space 2} .3462964{col 37}{space 1}   -6.58{col 46}{space 3}0.000{col 54}{space 4}-2.956014{col 67}{space 3}-1.598557
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4829832{col 26}{space 2} .1242624{col 37}{space 1}   -3.89{col 46}{space 3}0.000{col 54}{space 4} -.726533{col 67}{space 3}-.2394334
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2} -1.35257{col 26}{space 2} .1573011{col 37}{space 1}   -8.60{col 46}{space 3}0.000{col 54}{space 4}-1.660874{col 67}{space 3}-1.044265
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.9929671{col 26}{space 2} .1378948{col 37}{space 1}   -7.20{col 46}{space 3}0.000{col 54}{space 4}-1.263236{col 67}{space 3}-.7226983
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}8  {c |}{col 14}{res}{space 2}-1.069977{col 26}{space 2} .1859554{col 37}{space 1}   -5.75{col 46}{space 3}0.000{col 54}{space 4}-1.434443{col 67}{space 3}-.7055115
{txt}{space 10}9  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}10  {c |}{col 14}{res}{space 2}-1.540271{col 26}{space 2} .1915199{col 37}{space 1}   -8.04{col 46}{space 3}0.000{col 54}{space 4}-1.915644{col 67}{space 3}-1.164899
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.5689145{col 26}{space 2} .1125564{col 37}{space 1}   -5.05{col 46}{space 3}0.000{col 54}{space 4}-.7895209{col 67}{space 3}-.3483081
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-1.200337{col 26}{space 2} .1525831{col 37}{space 1}   -7.87{col 46}{space 3}0.000{col 54}{space 4}-1.499394{col 67}{space 3}-.9012792
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.0172339{col 26}{space 2} .1212748{col 37}{space 1}   -0.14{col 46}{space 3}0.887{col 54}{space 4}-.2549282{col 67}{space 3} .2204604
{txt}{space 9}15  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}16  {c |}{col 14}{res}{space 2}-2.919786{col 26}{space 2} .3883112{col 37}{space 1}   -7.52{col 46}{space 3}0.000{col 54}{space 4}-3.680862{col 67}{space 3} -2.15871
{txt}{space 9}17  {c |}{col 14}{res}{space 2} -1.10325{col 26}{space 2} .1472464{col 37}{space 1}   -7.49{col 46}{space 3}0.000{col 54}{space 4}-1.391848{col 67}{space 3}-.8146523
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2}-.4485598{col 26}{space 2} .1299297{col 37}{space 1}   -3.45{col 46}{space 3}0.001{col 54}{space 4}-.7032174{col 67}{space 3}-.1939022
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.3870096{col 26}{space 2} .1211277{col 37}{space 1}   -3.20{col 46}{space 3}0.001{col 54}{space 4}-.6244155{col 67}{space 3}-.1496037
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.7406281{col 26}{space 2}  .142584{col 37}{space 1}   -5.19{col 46}{space 3}0.000{col 54}{space 4}-1.020088{col 67}{space 3}-.4611687
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.9773488{col 26}{space 2} .1324458{col 37}{space 1}   -7.38{col 46}{space 3}0.000{col 54}{space 4}-1.236938{col 67}{space 3}-.7177598
{txt}{space 9}23  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}24  {c |}{col 14}{res}{space 2}-.4470903{col 26}{space 2} .1226055{col 37}{space 1}   -3.65{col 46}{space 3}0.000{col 54}{space 4}-.6873927{col 67}{space 3}-.2067879
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-1.223889{col 26}{space 2}  .211358{col 37}{space 1}   -5.79{col 46}{space 3}0.000{col 54}{space 4}-1.638143{col 67}{space 3}-.8096351
{txt}{space 9}26  {c |}{col 14}{res}{space 2} -1.20847{col 26}{space 2} .1754564{col 37}{space 1}   -6.89{col 46}{space 3}0.000{col 54}{space 4}-1.552358{col 67}{space 3}-.8645819
{txt}{space 9}27  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}28  {c |}{col 14}{res}{space 2} .4315535{col 26}{space 2} .1078451{col 37}{space 1}    4.00{col 46}{space 3}0.000{col 54}{space 4} .2201811{col 67}{space 3}  .642926
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.2696397{col 26}{space 2} .1041131{col 37}{space 1}   -2.59{col 46}{space 3}0.010{col 54}{space 4}-.4736977{col 67}{space 3}-.0655817
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-1.149694{col 26}{space 2} .1628974{col 37}{space 1}   -7.06{col 46}{space 3}0.000{col 54}{space 4}-1.468967{col 67}{space 3}-.8304211
{txt}{space 9}31  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.303728{col 26}{space 2} .3496081{col 37}{space 1}   -3.73{col 46}{space 3}0.000{col 54}{space 4}-1.988947{col 67}{space 3}-.6185087
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict left2logit
{txt}(option {bf:pr} assumed; Pr(left2))
(17,177 missing values generated)

{com}. 
. logit green2 education agea female bebe minority   y uemp3m  i.source  polintr   i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1561 obs not used

note: 5.election != 0 predicts failure perfectly
      5.election dropped and 1672 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1490 obs not used

note: 21.election != 0 predicts failure perfectly
      21.election dropped and 1161 obs not used

note: 25.election != 0 predicts failure perfectly
      25.election dropped and 584 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-8466.7241}  
Iteration 1:{space 3}log likelihood = {res:-7791.1255}  
Iteration 2:{space 3}log likelihood = {res:-7664.9047}  
Iteration 3:{space 3}log likelihood = {res:-7663.3149}  
Iteration 4:{space 3}log likelihood = {res:-7663.3092}  
Iteration 5:{space 3}log likelihood = {res:-7663.3092}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    34,172
{txt}{col 49}LR chi2({res}35{txt}){col 67}= {res}   1606.83
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7663.3092{txt}{col 49}Pseudo R2{col 67}= {res}    0.0949

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      green2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .3268122{col 26}{space 2} .0207803{col 37}{space 1}   15.73{col 46}{space 3}0.000{col 54}{space 4} .2860836{col 67}{space 3} .3675408
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0173484{col 26}{space 2} .0018219{col 37}{space 1}   -9.52{col 46}{space 3}0.000{col 54}{space 4}-.0209193{col 67}{space 3}-.0137775
{txt}{space 6}female {c |}{col 14}{res}{space 2} .2444317{col 26}{space 2} .0453488{col 37}{space 1}    5.39{col 46}{space 3}0.000{col 54}{space 4} .1555497{col 67}{space 3} .3333138
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0070324{col 26}{space 2} .0480291{col 37}{space 1}    0.15{col 46}{space 3}0.884{col 54}{space 4}-.0871029{col 67}{space 3} .1011677
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.1256214{col 26}{space 2} .1362958{col 37}{space 1}   -0.92{col 46}{space 3}0.357{col 54}{space 4}-.3927562{col 67}{space 3} .1415134
{txt}{space 11}y {c |}{col 14}{res}{space 2}-.0025093{col 26}{space 2} .0350271{col 37}{space 1}   -0.07{col 46}{space 3}0.943{col 54}{space 4}-.0711611{col 67}{space 3} .0661425
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2} -.242182{col 26}{space 2} .0505658{col 37}{space 1}   -4.79{col 46}{space 3}0.000{col 54}{space 4}-.3412892{col 67}{space 3}-.1430748
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1606394{col 26}{space 2} .0713627{col 37}{space 1}   -2.25{col 46}{space 3}0.024{col 54}{space 4}-.3005077{col 67}{space 3}-.0207712
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2032554{col 26}{space 2} .1234823{col 37}{space 1}    1.65{col 46}{space 3}0.100{col 54}{space 4}-.0387655{col 67}{space 3} .4452764
{txt}{space 10}3  {c |}{col 14}{res}{space 2} -.136685{col 26}{space 2} .0958575{col 37}{space 1}   -1.43{col 46}{space 3}0.154{col 54}{space 4}-.3245622{col 67}{space 3} .0511923
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.1992017{col 26}{space 2} .0292537{col 37}{space 1}   -6.81{col 46}{space 3}0.000{col 54}{space 4}-.2565378{col 67}{space 3}-.1418655
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}  1.60205{col 26}{space 2} .2870084{col 37}{space 1}    5.58{col 46}{space 3}0.000{col 54}{space 4} 1.039524{col 67}{space 3} 2.164576
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.886316{col 26}{space 2} .2483995{col 37}{space 1}    7.59{col 46}{space 3}0.000{col 54}{space 4} 1.399462{col 67}{space 3}  2.37317
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}6  {c |}{col 14}{res}{space 2} 1.978449{col 26}{space 2} .2452897{col 37}{space 1}    8.07{col 46}{space 3}0.000{col 54}{space 4}  1.49769{col 67}{space 3} 2.459208
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .5822297{col 26}{space 2} .2932493{col 37}{space 1}    1.99{col 46}{space 3}0.047{col 54}{space 4} .0074717{col 67}{space 3} 1.156988
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .5521813{col 26}{space 2} .3469847{col 37}{space 1}    1.59{col 46}{space 3}0.112{col 54}{space 4}-.1278962{col 67}{space 3} 1.232259
{txt}{space 10}9  {c |}{col 14}{res}{space 2} 2.480597{col 26}{space 2} .2410876{col 37}{space 1}   10.29{col 46}{space 3}0.000{col 54}{space 4} 2.008074{col 67}{space 3}  2.95312
{txt}{space 9}10  {c |}{col 14}{res}{space 2} 1.417378{col 26}{space 2} .2856411{col 37}{space 1}    4.96{col 46}{space 3}0.000{col 54}{space 4} .8575317{col 67}{space 3} 1.977224
{txt}{space 9}11  {c |}{col 14}{res}{space 2} 2.378257{col 26}{space 2} .2362873{col 37}{space 1}   10.07{col 46}{space 3}0.000{col 54}{space 4} 1.915142{col 67}{space 3} 2.841371
{txt}{space 9}12  {c |}{col 14}{res}{space 2}   1.2892{col 26}{space 2}   .26209{col 37}{space 1}    4.92{col 46}{space 3}0.000{col 54}{space 4} .7755135{col 67}{space 3} 1.802887
{txt}{space 9}13  {c |}{col 14}{res}{space 2} 1.055599{col 26}{space 2} .2758388{col 37}{space 1}    3.83{col 46}{space 3}0.000{col 54}{space 4} .5149647{col 67}{space 3} 1.596233
{txt}{space 9}15  {c |}{col 14}{res}{space 2} 2.645724{col 26}{space 2} .2399518{col 37}{space 1}   11.03{col 46}{space 3}0.000{col 54}{space 4} 2.175427{col 67}{space 3} 3.116021
{txt}{space 9}16  {c |}{col 14}{res}{space 2} 1.944859{col 26}{space 2} .2606686{col 37}{space 1}    7.46{col 46}{space 3}0.000{col 54}{space 4} 1.433958{col 67}{space 3}  2.45576
{txt}{space 9}17  {c |}{col 14}{res}{space 2}  1.39222{col 26}{space 2} .2579647{col 37}{space 1}    5.40{col 46}{space 3}0.000{col 54}{space 4} .8866181{col 67}{space 3} 1.897821
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2} .6562328{col 26}{space 2} .2924895{col 37}{space 1}    2.24{col 46}{space 3}0.025{col 54}{space 4} .0829639{col 67}{space 3} 1.229502
{txt}{space 9}20  {c |}{col 14}{res}{space 2} 1.492065{col 26}{space 2} .2539881{col 37}{space 1}    5.87{col 46}{space 3}0.000{col 54}{space 4}  .994257{col 67}{space 3} 1.989872
{txt}{space 9}21  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}22  {c |}{col 14}{res}{space 2}  2.12331{col 26}{space 2} .2419376{col 37}{space 1}    8.78{col 46}{space 3}0.000{col 54}{space 4} 1.649121{col 67}{space 3} 2.597499
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .4807015{col 26}{space 2} .2913199{col 37}{space 1}    1.65{col 46}{space 3}0.099{col 54}{space 4}-.0902749{col 67}{space 3} 1.051678
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .7434443{col 26}{space 2} .2813531{col 37}{space 1}    2.64{col 46}{space 3}0.008{col 54}{space 4} .1920024{col 67}{space 3} 1.294886
{txt}{space 9}25  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}26  {c |}{col 14}{res}{space 2} 1.704568{col 26}{space 2} .2753115{col 37}{space 1}    6.19{col 46}{space 3}0.000{col 54}{space 4} 1.164967{col 67}{space 3} 2.244168
{txt}{space 9}27  {c |}{col 14}{res}{space 2}  2.60094{col 26}{space 2} .2427897{col 37}{space 1}   10.71{col 46}{space 3}0.000{col 54}{space 4} 2.125081{col 67}{space 3} 3.076799
{txt}{space 9}28  {c |}{col 14}{res}{space 2} 2.138698{col 26}{space 2} .2449468{col 37}{space 1}    8.73{col 46}{space 3}0.000{col 54}{space 4} 1.658611{col 67}{space 3} 2.618785
{txt}{space 9}29  {c |}{col 14}{res}{space 2} 2.012408{col 26}{space 2} .2376571{col 37}{space 1}    8.47{col 46}{space 3}0.000{col 54}{space 4} 1.546608{col 67}{space 3} 2.478207
{txt}{space 9}30  {c |}{col 14}{res}{space 2} 1.711748{col 26}{space 2} .2589133{col 37}{space 1}    6.61{col 46}{space 3}0.000{col 54}{space 4} 1.204288{col 67}{space 3} 2.219209
{txt}{space 9}31  {c |}{col 14}{res}{space 2} 1.964201{col 26}{space 2}  .244364{col 37}{space 1}    8.04{col 46}{space 3}0.000{col 54}{space 4} 1.485256{col 67}{space 3} 2.443146
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-3.533983{col 26}{space 2} .4057359{col 37}{space 1}   -8.71{col 46}{space 3}0.000{col 54}{space 4}-4.329211{col 67}{space 3}-2.738755
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict green2logit
{txt}(option {bf:pr} assumed; Pr(green2))
(11,197 missing values generated)

{com}. 
. *3. With logit with simulated turnout, it's the same than normal logit except that we remove likely abstainers
. 
. logit voting2  education agea female bebe minority y uemp3m i.source  polintr i.election if voting==1 // A regression predicting turnout

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-10494.093}  
Iteration 1:{space 3}log likelihood = {res:-8961.3462}  
Iteration 2:{space 3}log likelihood = {res:-6617.2899}  
Iteration 3:{space 3}log likelihood = {res:-6406.6294}  
Iteration 4:{space 3}log likelihood = {res:-6404.5543}  
Iteration 5:{space 3}log likelihood = {res:-6404.5485}  
Iteration 6:{space 3}log likelihood = {res:-6404.5485}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    43,480
{txt}{col 49}LR chi2({res}40{txt}){col 67}= {res}   8179.09
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-6404.5485{txt}{col 49}Pseudo R2{col 67}= {res}    0.3897

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     voting2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2}  .414348{col 26}{space 2} .0241182{col 37}{space 1}   17.18{col 46}{space 3}0.000{col 54}{space 4} .3670773{col 67}{space 3} .4616187
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0110554{col 26}{space 2} .0020053{col 37}{space 1}    5.51{col 46}{space 3}0.000{col 54}{space 4} .0071251{col 67}{space 3} .0149856
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0252858{col 26}{space 2} .0488097{col 37}{space 1}    0.52{col 46}{space 3}0.604{col 54}{space 4}-.0703794{col 67}{space 3} .1209511
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}-.0057225{col 26}{space 2} .0524267{col 37}{space 1}   -0.11{col 46}{space 3}0.913{col 54}{space 4}-.1084768{col 67}{space 3} .0970319
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0153869{col 26}{space 2} .1323716{col 37}{space 1}   -0.12{col 46}{space 3}0.907{col 54}{space 4}-.2748305{col 67}{space 3} .2440566
{txt}{space 11}y {c |}{col 14}{res}{space 2}-2.037587{col 26}{space 2} .0346249{col 37}{space 1}  -58.85{col 46}{space 3}0.000{col 54}{space 4} -2.10545{col 67}{space 3}-1.969723
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.0030876{col 26}{space 2} .0535168{col 37}{space 1}   -0.06{col 46}{space 3}0.954{col 54}{space 4}-.1079785{col 67}{space 3} .1018033
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0623188{col 26}{space 2} .0823601{col 37}{space 1}   -0.76{col 46}{space 3}0.449{col 54}{space 4}-.2237417{col 67}{space 3} .0991041
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0793152{col 26}{space 2} .1067369{col 37}{space 1}    0.74{col 46}{space 3}0.457{col 54}{space 4}-.1298853{col 67}{space 3} .2885158
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0569228{col 26}{space 2} .0946898{col 37}{space 1}   -0.60{col 46}{space 3}0.548{col 54}{space 4}-.2425115{col 67}{space 3} .1286658
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.0076027{col 26}{space 2}   .02725{col 37}{space 1}   -0.28{col 46}{space 3}0.780{col 54}{space 4}-.0610117{col 67}{space 3} .0458063
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.4284477{col 26}{space 2} .4158218{col 37}{space 1}   -1.03{col 46}{space 3}0.303{col 54}{space 4}-1.243443{col 67}{space 3}  .386548
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.5995002{col 26}{space 2} .3559484{col 37}{space 1}   -1.68{col 46}{space 3}0.092{col 54}{space 4}-1.297146{col 67}{space 3} .0981459
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  -.19828{col 26}{space 2} .3569751{col 37}{space 1}   -0.56{col 46}{space 3}0.579{col 54}{space 4}-.8979383{col 67}{space 3} .5013783
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.5330487{col 26}{space 2} .3360195{col 37}{space 1}   -1.59{col 46}{space 3}0.113{col 54}{space 4}-1.191635{col 67}{space 3} .1255375
{txt}{space 10}6  {c |}{col 14}{res}{space 2} -.501829{col 26}{space 2} .3492296{col 37}{space 1}   -1.44{col 46}{space 3}0.151{col 54}{space 4}-1.186306{col 67}{space 3} .1826484
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.3004274{col 26}{space 2} .3533903{col 37}{space 1}   -0.85{col 46}{space 3}0.395{col 54}{space 4}-.9930596{col 67}{space 3} .3922048
{txt}{space 10}8  {c |}{col 14}{res}{space 2} -.653052{col 26}{space 2} .3566643{col 37}{space 1}   -1.83{col 46}{space 3}0.067{col 54}{space 4}-1.352101{col 67}{space 3} .0459972
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.7022622{col 26}{space 2} .3433007{col 37}{space 1}   -2.05{col 46}{space 3}0.041{col 54}{space 4}-1.375119{col 67}{space 3}-.0294052
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.5922361{col 26}{space 2} .3287903{col 37}{space 1}   -1.80{col 46}{space 3}0.072{col 54}{space 4}-1.236653{col 67}{space 3} .0521811
{txt}{space 9}11  {c |}{col 14}{res}{space 2} -.963022{col 26}{space 2} .3363183{col 37}{space 1}   -2.86{col 46}{space 3}0.004{col 54}{space 4}-1.622194{col 67}{space 3}-.3038502
{txt}{space 9}12  {c |}{col 14}{res}{space 2} -.698155{col 26}{space 2} .3501161{col 37}{space 1}   -1.99{col 46}{space 3}0.046{col 54}{space 4} -1.38437{col 67}{space 3}-.0119402
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.4855858{col 26}{space 2} .3274721{col 37}{space 1}   -1.48{col 46}{space 3}0.138{col 54}{space 4}-1.127419{col 67}{space 3} .1562478
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.8058098{col 26}{space 2} .3389377{col 37}{space 1}   -2.38{col 46}{space 3}0.017{col 54}{space 4}-1.470116{col 67}{space 3}-.1415041
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.2241423{col 26}{space 2} .3967827{col 37}{space 1}   -0.56{col 46}{space 3}0.572{col 54}{space 4}-1.001822{col 67}{space 3} .5535374
{txt}{space 9}17  {c |}{col 14}{res}{space 2} -.668471{col 26}{space 2}  .367077{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4}-1.387929{col 67}{space 3} .0509868
{txt}{space 9}18  {c |}{col 14}{res}{space 2}-.8507854{col 26}{space 2} .3382777{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-1.513798{col 67}{space 3}-.1877733
{txt}{space 9}19  {c |}{col 14}{res}{space 2}-.6120046{col 26}{space 2} .3236437{col 37}{space 1}   -1.89{col 46}{space 3}0.059{col 54}{space 4}-1.246335{col 67}{space 3} .0223254
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .0176174{col 26}{space 2} .4283003{col 37}{space 1}    0.04{col 46}{space 3}0.967{col 54}{space 4}-.8218358{col 67}{space 3} .8570706
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.7829962{col 26}{space 2} .3346743{col 37}{space 1}   -2.34{col 46}{space 3}0.019{col 54}{space 4}-1.438946{col 67}{space 3}-.1270466
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.7364591{col 26}{space 2} .3427002{col 37}{space 1}   -2.15{col 46}{space 3}0.032{col 54}{space 4}-1.408139{col 67}{space 3} -.064779
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.9016864{col 26}{space 2} .3295253{col 37}{space 1}   -2.74{col 46}{space 3}0.006{col 54}{space 4}-1.547544{col 67}{space 3}-.2558287
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.6635598{col 26}{space 2} .3534557{col 37}{space 1}   -1.88{col 46}{space 3}0.060{col 54}{space 4} -1.35632{col 67}{space 3} .0292007
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-.9748555{col 26}{space 2} .3575057{col 37}{space 1}   -2.73{col 46}{space 3}0.006{col 54}{space 4}-1.675554{col 67}{space 3}-.2741572
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.8652673{col 26}{space 2} .3275103{col 37}{space 1}   -2.64{col 46}{space 3}0.008{col 54}{space 4}-1.507176{col 67}{space 3}-.2233589
{txt}{space 9}27  {c |}{col 14}{res}{space 2}-.9388356{col 26}{space 2} .3503052{col 37}{space 1}   -2.68{col 46}{space 3}0.007{col 54}{space 4}-1.625421{col 67}{space 3}  -.25225
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.9152291{col 26}{space 2} .3391257{col 37}{space 1}   -2.70{col 46}{space 3}0.007{col 54}{space 4}-1.579903{col 67}{space 3}-.2505549
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.6396817{col 26}{space 2} .3472836{col 37}{space 1}   -1.84{col 46}{space 3}0.065{col 54}{space 4}-1.320345{col 67}{space 3} .0409818
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-.9696245{col 26}{space 2} .3491259{col 37}{space 1}   -2.78{col 46}{space 3}0.005{col 54}{space 4}-1.653899{col 67}{space 3}-.2853503
{txt}{space 9}31  {c |}{col 14}{res}{space 2}-.7042488{col 26}{space 2} .3703414{col 37}{space 1}   -1.90{col 46}{space 3}0.057{col 54}{space 4}-1.430104{col 67}{space 3}  .021607
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 7.301768{col 26}{space 2} .4594933{col 37}{space 1}   15.89{col 46}{space 3}0.000{col 54}{space 4} 6.401178{col 67}{space 3} 8.202359
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict voting2logit
{txt}(option {bf:pr} assumed; Pr(voting2))
(2,083 missing values generated)

{com}. 
. gen likely_abstainer=1 if voting2logit<0.5 // People who have a prediction of voting lower than 0.5 are likely abstainers
{txt}(53,389 missing values generated)

{com}. replace likely_abstainer=0 if voting2logit>=0.5
{txt}(53,389 real changes made)

{com}. 
. tab likely_abstainer if inclusion==1 // Almost noone. But It's normmal, because everybody in the sample of 3000 voters voted. When you do it in Table 1, some will be likely abstainers, so this step matters.

{txt}likely_abst {c |}
      ainer {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      2,291       76.37       76.37
{txt}          1 {c |}{res}        709       23.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      3,000      100.00
{txt}
{com}. 
. logit PS2 education agea female bebe minority y uemp3m i.source polintr i.election if likely_abstainer==0 // We simply exlcude those likely abstainers

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-22201.681}  
Iteration 1:{space 3}log likelihood = {res:-21412.758}  
Iteration 2:{space 3}log likelihood = {res:-21389.477}  
Iteration 3:{space 3}log likelihood = {res:-21389.337}  
Iteration 4:{space 3}log likelihood = {res:-21389.337}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    39,563
{txt}{col 49}LR chi2({res}40{txt}){col 67}= {res}   1624.69
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-21389.337{txt}{col 49}Pseudo R2{col 67}= {res}    0.0366

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         PS2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2}-.1127536{col 26}{space 2} .0100951{col 37}{space 1}  -11.17{col 46}{space 3}0.000{col 54}{space 4}-.1325396{col 67}{space 3}-.0929675
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0026225{col 26}{space 2} .0009896{col 37}{space 1}    2.65{col 46}{space 3}0.008{col 54}{space 4} .0006828{col 67}{space 3} .0045621
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0560444{col 26}{space 2} .0242079{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4} .0085977{col 67}{space 3} .1034911
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}-.0056621{col 26}{space 2} .0267771{col 37}{space 1}   -0.21{col 46}{space 3}0.833{col 54}{space 4}-.0581441{col 67}{space 3}   .04682
{txt}{space 4}minority {c |}{col 14}{res}{space 2} -.575902{col 26}{space 2} .0711657{col 37}{space 1}   -8.09{col 46}{space 3}0.000{col 54}{space 4}-.7153843{col 67}{space 3}-.4364197
{txt}{space 11}y {c |}{col 14}{res}{space 2} .0693768{col 26}{space 2} .0198826{col 37}{space 1}    3.49{col 46}{space 3}0.000{col 54}{space 4} .0304077{col 67}{space 3} .1083459
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.1472399{col 26}{space 2} .0282097{col 37}{space 1}   -5.22{col 46}{space 3}0.000{col 54}{space 4}  -.20253{col 67}{space 3}-.0919499
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5639818{col 26}{space 2} .0450687{col 37}{space 1}   12.51{col 46}{space 3}0.000{col 54}{space 4} .4756488{col 67}{space 3} .6523148
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5501475{col 26}{space 2} .0774742{col 37}{space 1}    7.10{col 46}{space 3}0.000{col 54}{space 4} .3983009{col 67}{space 3}  .701994
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .5631435{col 26}{space 2} .0528106{col 37}{space 1}   10.66{col 46}{space 3}0.000{col 54}{space 4} .4596366{col 67}{space 3} .6666504
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.0931776{col 26}{space 2} .0147724{col 37}{space 1}   -6.31{col 46}{space 3}0.000{col 54}{space 4}-.1221311{col 67}{space 3}-.0642241
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.2556096{col 26}{space 2} .1156967{col 37}{space 1}   -2.21{col 46}{space 3}0.027{col 54}{space 4} -.482371{col 67}{space 3}-.0288482
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4965513{col 26}{space 2} .0941863{col 37}{space 1}   -5.27{col 46}{space 3}0.000{col 54}{space 4}-.6811531{col 67}{space 3}-.3119495
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5725129{col 26}{space 2} .0817981{col 37}{space 1}    7.00{col 46}{space 3}0.000{col 54}{space 4} .4121915{col 67}{space 3} .7328343
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .6463318{col 26}{space 2} .0819817{col 37}{space 1}    7.88{col 46}{space 3}0.000{col 54}{space 4} .4856507{col 67}{space 3}  .807013
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.3845654{col 26}{space 2} .0904645{col 37}{space 1}   -4.25{col 46}{space 3}0.000{col 54}{space 4}-.5618726{col 67}{space 3}-.2072582
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-1.326601{col 26}{space 2} .1210981{col 37}{space 1}  -10.95{col 46}{space 3}0.000{col 54}{space 4}-1.563949{col 67}{space 3}-1.089253
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.4012297{col 26}{space 2} .1111738{col 37}{space 1}   -3.61{col 46}{space 3}0.000{col 54}{space 4}-.6191264{col 67}{space 3}-.1833329
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1634762{col 26}{space 2} .0873287{col 37}{space 1}    1.87{col 46}{space 3}0.061{col 54}{space 4} -.007685{col 67}{space 3} .3346373
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0022258{col 26}{space 2} .0931418{col 37}{space 1}    0.02{col 46}{space 3}0.981{col 54}{space 4}-.1803286{col 67}{space 3} .1847803
{txt}{space 9}11  {c |}{col 14}{res}{space 2} -.134911{col 26}{space 2} .0818181{col 37}{space 1}   -1.65{col 46}{space 3}0.099{col 54}{space 4}-.2952715{col 67}{space 3} .0254495
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1370977{col 26}{space 2} .0869051{col 37}{space 1}    1.58{col 46}{space 3}0.115{col 54}{space 4}-.0332332{col 67}{space 3} .3074285
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3582362{col 26}{space 2}  .091422{col 37}{space 1}    3.92{col 46}{space 3}0.000{col 54}{space 4} .1790524{col 67}{space 3}   .53742
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.2365926{col 26}{space 2} .0929752{col 37}{space 1}   -2.54{col 46}{space 3}0.011{col 54}{space 4}-.4188205{col 67}{space 3}-.0543646
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.5694364{col 26}{space 2} .1076495{col 37}{space 1}   -5.29{col 46}{space 3}0.000{col 54}{space 4}-.7804256{col 67}{space 3}-.3584473
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3601615{col 26}{space 2} .0840107{col 37}{space 1}    4.29{col 46}{space 3}0.000{col 54}{space 4} .1955035{col 67}{space 3} .5248194
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .0618463{col 26}{space 2} .0857933{col 37}{space 1}    0.72{col 46}{space 3}0.471{col 54}{space 4}-.1063055{col 67}{space 3} .2299981
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2605395{col 26}{space 2} .0912457{col 37}{space 1}    2.86{col 46}{space 3}0.004{col 54}{space 4} .0817012{col 67}{space 3} .4393777
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.0023372{col 26}{space 2} .0879963{col 37}{space 1}   -0.03{col 46}{space 3}0.979{col 54}{space 4}-.1748067{col 67}{space 3} .1701323
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.1765926{col 26}{space 2} .0958166{col 37}{space 1}   -1.84{col 46}{space 3}0.065{col 54}{space 4}-.3643897{col 67}{space 3} .0112046
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.5947574{col 26}{space 2} .0917281{col 37}{space 1}   -6.48{col 46}{space 3}0.000{col 54}{space 4}-.7745413{col 67}{space 3}-.4149735
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.7992759{col 26}{space 2} .0981634{col 37}{space 1}   -8.14{col 46}{space 3}0.000{col 54}{space 4}-.9916727{col 67}{space 3}-.6068791
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.1958566{col 26}{space 2} .0895155{col 37}{space 1}   -2.19{col 46}{space 3}0.029{col 54}{space 4}-.3713037{col 67}{space 3}-.0204095
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .1464575{col 26}{space 2} .1124071{col 37}{space 1}    1.30{col 46}{space 3}0.193{col 54}{space 4}-.0738564{col 67}{space 3} .3667713
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.2482615{col 26}{space 2} .0995383{col 37}{space 1}   -2.49{col 46}{space 3}0.013{col 54}{space 4} -.443353{col 67}{space 3}-.0531699
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .0822246{col 26}{space 2} .0900735{col 37}{space 1}    0.91{col 46}{space 3}0.361{col 54}{space 4}-.0943162{col 67}{space 3} .2587654
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.5305899{col 26}{space 2} .0978745{col 37}{space 1}   -5.42{col 46}{space 3}0.000{col 54}{space 4}-.7224204{col 67}{space 3}-.3387594
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.0744951{col 26}{space 2}  .079704{col 37}{space 1}   -0.93{col 46}{space 3}0.350{col 54}{space 4} -.230712{col 67}{space 3} .0817218
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1482752{col 26}{space 2} .0920258{col 37}{space 1}    1.61{col 46}{space 3}0.107{col 54}{space 4}-.0320921{col 67}{space 3} .3286425
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .0565951{col 26}{space 2} .0860963{col 37}{space 1}    0.66{col 46}{space 3}0.511{col 54}{space 4}-.1121506{col 67}{space 3} .2253408
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.0213681{col 26}{space 2} .1894905{col 37}{space 1}   -0.11{col 46}{space 3}0.910{col 54}{space 4}-.3927626{col 67}{space 3} .3500264
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict PS2logit2
{txt}(option {bf:pr} assumed; Pr(PS2))
(2,083 missing values generated)

{com}. 
. logit left2 education agea female bebe minority   y uemp3m i.source  polintr    i.election if likely_abstainer==0 

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1545 obs not used

note: 7.election != 0 predicts failure perfectly
      7.election dropped and 1196 obs not used

note: 9.election != 0 predicts failure perfectly
      9.election dropped and 1353 obs not used

note: 15.election != 0 predicts failure perfectly
      15.election dropped and 1290 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1457 obs not used

note: 23.election != 0 predicts failure perfectly
      23.election dropped and 1519 obs not used

note: 27.election != 0 predicts failure perfectly
      27.election dropped and 1202 obs not used

note: 31.election != 0 predicts failure perfectly
      31.election dropped and 1446 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -7717.598}  
Iteration 1:{space 3}log likelihood = {res:-7287.8012}  
Iteration 2:{space 3}log likelihood = {res:-7196.8861}  
Iteration 3:{space 3}log likelihood = {res:-7194.7794}  
Iteration 4:{space 3}log likelihood = {res: -7194.751}  
Iteration 5:{space 3}log likelihood = {res: -7194.751}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    28,555
{txt}{col 49}LR chi2({res}32{txt}){col 67}= {res}   1045.69
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -7194.751{txt}{col 49}Pseudo R2{col 67}= {res}    0.0677

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       left2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .0422657{col 26}{space 2} .0196271{col 37}{space 1}    2.15{col 46}{space 3}0.031{col 54}{space 4} .0037972{col 67}{space 3} .0807342
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0073401{col 26}{space 2} .0018471{col 37}{space 1}   -3.97{col 46}{space 3}0.000{col 54}{space 4}-.0109602{col 67}{space 3}-.0037199
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0391694{col 26}{space 2} .0463623{col 37}{space 1}    0.84{col 46}{space 3}0.398{col 54}{space 4}-.0516992{col 67}{space 3} .1300379
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .1782925{col 26}{space 2} .0501954{col 37}{space 1}    3.55{col 46}{space 3}0.000{col 54}{space 4} .0799113{col 67}{space 3} .2766737
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.1299782{col 26}{space 2} .1435275{col 37}{space 1}   -0.91{col 46}{space 3}0.365{col 54}{space 4}-.4112869{col 67}{space 3} .1513305
{txt}{space 11}y {c |}{col 14}{res}{space 2} .2619233{col 26}{space 2} .0365687{col 37}{space 1}    7.16{col 46}{space 3}0.000{col 54}{space 4}   .19025{col 67}{space 3} .3335966
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}  -.35781{col 26}{space 2} .0497487{col 37}{space 1}   -7.19{col 46}{space 3}0.000{col 54}{space 4}-.4553157{col 67}{space 3}-.2603043
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4157323{col 26}{space 2} .0824873{col 37}{space 1}    5.04{col 46}{space 3}0.000{col 54}{space 4} .2540601{col 67}{space 3} .5774044
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6819257{col 26}{space 2} .1324021{col 37}{space 1}    5.15{col 46}{space 3}0.000{col 54}{space 4} .4224223{col 67}{space 3}  .941429
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1866912{col 26}{space 2} .1023225{col 37}{space 1}    1.82{col 46}{space 3}0.068{col 54}{space 4}-.0138572{col 67}{space 3} .3872397
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.2421892{col 26}{space 2} .0294054{col 37}{space 1}   -8.24{col 46}{space 3}0.000{col 54}{space 4}-.2998228{col 67}{space 3}-.1845555
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2} -2.27705{col 26}{space 2} .3464049{col 37}{space 1}   -6.57{col 46}{space 3}0.000{col 54}{space 4}-2.955991{col 67}{space 3}-1.598109
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.5162323{col 26}{space 2} .1254282{col 37}{space 1}   -4.12{col 46}{space 3}0.000{col 54}{space 4}-.7620671{col 67}{space 3}-.2703975
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2} -1.34757{col 26}{space 2} .1575199{col 37}{space 1}   -8.55{col 46}{space 3}0.000{col 54}{space 4}-1.656303{col 67}{space 3}-1.038836
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-1.014384{col 26}{space 2} .1386259{col 37}{space 1}   -7.32{col 46}{space 3}0.000{col 54}{space 4}-1.286086{col 67}{space 3}-.7426822
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}8  {c |}{col 14}{res}{space 2}-1.067027{col 26}{space 2} .1862492{col 37}{space 1}   -5.73{col 46}{space 3}0.000{col 54}{space 4}-1.432069{col 67}{space 3}-.7019856
{txt}{space 10}9  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}10  {c |}{col 14}{res}{space 2}-1.517619{col 26}{space 2} .1956484{col 37}{space 1}   -7.76{col 46}{space 3}0.000{col 54}{space 4}-1.901083{col 67}{space 3}-1.134155
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.5905151{col 26}{space 2} .1134496{col 37}{space 1}   -5.21{col 46}{space 3}0.000{col 54}{space 4}-.8128723{col 67}{space 3}-.3681579
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-1.212425{col 26}{space 2} .1527643{col 37}{space 1}   -7.94{col 46}{space 3}0.000{col 54}{space 4}-1.511838{col 67}{space 3}-.9130127
{txt}{space 9}13  {c |}{col 14}{res}{space 2}  .001791{col 26}{space 2}  .123255{col 37}{space 1}    0.01{col 46}{space 3}0.988{col 54}{space 4}-.2397843{col 67}{space 3} .2433663
{txt}{space 9}15  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}16  {c |}{col 14}{res}{space 2}-2.919257{col 26}{space 2} .3883573{col 37}{space 1}   -7.52{col 46}{space 3}0.000{col 54}{space 4}-3.680424{col 67}{space 3}-2.158091
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-1.121323{col 26}{space 2} .1480346{col 37}{space 1}   -7.57{col 46}{space 3}0.000{col 54}{space 4}-1.411466{col 67}{space 3}-.8311808
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2}-.4311368{col 26}{space 2} .1336348{col 37}{space 1}   -3.23{col 46}{space 3}0.001{col 54}{space 4}-.6930562{col 67}{space 3}-.1692174
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.3849515{col 26}{space 2} .1212082{col 37}{space 1}   -3.18{col 46}{space 3}0.001{col 54}{space 4}-.6225153{col 67}{space 3}-.1473877
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.7270839{col 26}{space 2} .1437339{col 37}{space 1}   -5.06{col 46}{space 3}0.000{col 54}{space 4}-1.008797{col 67}{space 3}-.4453706
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-1.013084{col 26}{space 2} .1338331{col 37}{space 1}   -7.57{col 46}{space 3}0.000{col 54}{space 4}-1.275392{col 67}{space 3}-.7507756
{txt}{space 9}23  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}24  {c |}{col 14}{res}{space 2}-.4682452{col 26}{space 2}   .12344{col 37}{space 1}   -3.79{col 46}{space 3}0.000{col 54}{space 4}-.7101831{col 67}{space 3}-.2263073
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-1.254298{col 26}{space 2} .2146262{col 37}{space 1}   -5.84{col 46}{space 3}0.000{col 54}{space 4}-1.674958{col 67}{space 3}-.8336386
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-1.238428{col 26}{space 2} .1813853{col 37}{space 1}   -6.83{col 46}{space 3}0.000{col 54}{space 4}-1.593937{col 67}{space 3}-.8829194
{txt}{space 9}27  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}28  {c |}{col 14}{res}{space 2} .4062574{col 26}{space 2} .1085678{col 37}{space 1}    3.74{col 46}{space 3}0.000{col 54}{space 4} .1934685{col 67}{space 3} .6190463
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.2676391{col 26}{space 2} .1042203{col 37}{space 1}   -2.57{col 46}{space 3}0.010{col 54}{space 4}-.4719072{col 67}{space 3} -.063371
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-1.147237{col 26}{space 2} .1630249{col 37}{space 1}   -7.04{col 46}{space 3}0.000{col 54}{space 4} -1.46676{col 67}{space 3}-.8277146
{txt}{space 9}31  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.287033{col 26}{space 2} .3583695{col 37}{space 1}   -3.59{col 46}{space 3}0.000{col 54}{space 4}-1.989425{col 67}{space 3}-.5846421
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict left2logit2
{txt}(option {bf:pr} assumed; Pr(left2))
(17,177 missing values generated)

{com}. 
. logit green2 education agea female bebe minority   y uemp3m  i.source  polintr   i.election if likely_abstainer==0 

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1545 obs not used

note: 5.election != 0 predicts failure perfectly
      5.election dropped and 1641 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1457 obs not used

note: 21.election != 0 predicts failure perfectly
      21.election dropped and 1116 obs not used

note: 25.election != 0 predicts failure perfectly
      25.election dropped and 572 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-8306.2153}  
Iteration 1:{space 3}log likelihood = {res:-7644.9129}  
Iteration 2:{space 3}log likelihood = {res:-7521.9757}  
Iteration 3:{space 3}log likelihood = {res:-7520.3932}  
Iteration 4:{space 3}log likelihood = {res:-7520.3869}  
Iteration 5:{space 3}log likelihood = {res:-7520.3869}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    33,232
{txt}{col 49}LR chi2({res}35{txt}){col 67}= {res}   1571.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7520.3869{txt}{col 49}Pseudo R2{col 67}= {res}    0.0946

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      green2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .3303074{col 26}{space 2} .0209634{col 37}{space 1}   15.76{col 46}{space 3}0.000{col 54}{space 4} .2892199{col 67}{space 3} .3713948
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0176237{col 26}{space 2} .0018418{col 37}{space 1}   -9.57{col 46}{space 3}0.000{col 54}{space 4}-.0212335{col 67}{space 3}-.0140139
{txt}{space 6}female {c |}{col 14}{res}{space 2} .2411532{col 26}{space 2}  .045709{col 37}{space 1}    5.28{col 46}{space 3}0.000{col 54}{space 4} .1515653{col 67}{space 3} .3307411
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0044091{col 26}{space 2} .0484455{col 37}{space 1}    0.09{col 46}{space 3}0.927{col 54}{space 4}-.0905423{col 67}{space 3} .0993605
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.1487238{col 26}{space 2} .1375665{col 37}{space 1}   -1.08{col 46}{space 3}0.280{col 54}{space 4}-.4183491{col 67}{space 3} .1209016
{txt}{space 11}y {c |}{col 14}{res}{space 2}-.0099949{col 26}{space 2} .0372843{col 37}{space 1}   -0.27{col 46}{space 3}0.789{col 54}{space 4}-.0830708{col 67}{space 3} .0630811
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.2428447{col 26}{space 2} .0510594{col 37}{space 1}   -4.76{col 46}{space 3}0.000{col 54}{space 4}-.3429192{col 67}{space 3}-.1427702
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1480756{col 26}{space 2} .0721484{col 37}{space 1}   -2.05{col 46}{space 3}0.040{col 54}{space 4}-.2894838{col 67}{space 3}-.0066673
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2032849{col 26}{space 2} .1280356{col 37}{space 1}    1.59{col 46}{space 3}0.112{col 54}{space 4}-.0476602{col 67}{space 3}   .45423
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1179232{col 26}{space 2} .0971048{col 37}{space 1}   -1.21{col 46}{space 3}0.225{col 54}{space 4}-.3082451{col 67}{space 3} .0723988
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.1984485{col 26}{space 2} .0295973{col 37}{space 1}   -6.70{col 46}{space 3}0.000{col 54}{space 4}-.2564581{col 67}{space 3}-.1404389
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.612123{col 26}{space 2} .2870839{col 37}{space 1}    5.62{col 46}{space 3}0.000{col 54}{space 4} 1.049449{col 67}{space 3} 2.174797
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.888919{col 26}{space 2} .2486137{col 37}{space 1}    7.60{col 46}{space 3}0.000{col 54}{space 4} 1.401645{col 67}{space 3} 2.376193
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}6  {c |}{col 14}{res}{space 2} 1.987457{col 26}{space 2} .2453802{col 37}{space 1}    8.10{col 46}{space 3}0.000{col 54}{space 4}  1.50652{col 67}{space 3} 2.468393
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .5657054{col 26}{space 2} .2952955{col 37}{space 1}    1.92{col 46}{space 3}0.055{col 54}{space 4}-.0130632{col 67}{space 3} 1.144474
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .5728321{col 26}{space 2} .3470709{col 37}{space 1}    1.65{col 46}{space 3}0.099{col 54}{space 4}-.1074144{col 67}{space 3} 1.253079
{txt}{space 10}9  {c |}{col 14}{res}{space 2} 2.477216{col 26}{space 2} .2414781{col 37}{space 1}   10.26{col 46}{space 3}0.000{col 54}{space 4} 2.003928{col 67}{space 3} 2.950505
{txt}{space 9}10  {c |}{col 14}{res}{space 2} 1.430952{col 26}{space 2} .2885457{col 37}{space 1}    4.96{col 46}{space 3}0.000{col 54}{space 4} .8654129{col 67}{space 3} 1.996491
{txt}{space 9}11  {c |}{col 14}{res}{space 2} 2.385207{col 26}{space 2} .2364214{col 37}{space 1}   10.09{col 46}{space 3}0.000{col 54}{space 4}  1.92183{col 67}{space 3} 2.848584
{txt}{space 9}12  {c |}{col 14}{res}{space 2} 1.293731{col 26}{space 2} .2621436{col 37}{space 1}    4.94{col 46}{space 3}0.000{col 54}{space 4} .7799387{col 67}{space 3} 1.807523
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .9737379{col 26}{space 2} .2820539{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .4209224{col 67}{space 3} 1.526553
{txt}{space 9}15  {c |}{col 14}{res}{space 2} 2.662724{col 26}{space 2}  .240069{col 37}{space 1}   11.09{col 46}{space 3}0.000{col 54}{space 4} 2.192197{col 67}{space 3}  3.13325
{txt}{space 9}16  {c |}{col 14}{res}{space 2} 1.951109{col 26}{space 2} .2607251{col 37}{space 1}    7.48{col 46}{space 3}0.000{col 54}{space 4} 1.440097{col 67}{space 3} 2.462121
{txt}{space 9}17  {c |}{col 14}{res}{space 2} 1.379971{col 26}{space 2} .2584004{col 37}{space 1}    5.34{col 46}{space 3}0.000{col 54}{space 4} .8735152{col 67}{space 3} 1.886426
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2} .7337678{col 26}{space 2} .2941857{col 37}{space 1}    2.49{col 46}{space 3}0.013{col 54}{space 4} .1571744{col 67}{space 3} 1.310361
{txt}{space 9}20  {c |}{col 14}{res}{space 2} 1.495553{col 26}{space 2} .2540078{col 37}{space 1}    5.89{col 46}{space 3}0.000{col 54}{space 4} .9977067{col 67}{space 3} 1.993399
{txt}{space 9}21  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}22  {c |}{col 14}{res}{space 2} 2.131223{col 26}{space 2}  .242102{col 37}{space 1}    8.80{col 46}{space 3}0.000{col 54}{space 4} 1.656712{col 67}{space 3} 2.605734
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .4339733{col 26}{space 2} .2971442{col 37}{space 1}    1.46{col 46}{space 3}0.144{col 54}{space 4}-.1484187{col 67}{space 3} 1.016365
{txt}{space 9}24  {c |}{col 14}{res}{space 2}  .755577{col 26}{space 2} .2813817{col 37}{space 1}    2.69{col 46}{space 3}0.007{col 54}{space 4}  .204079{col 67}{space 3} 1.307075
{txt}{space 9}25  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}26  {c |}{col 14}{res}{space 2} 1.675505{col 26}{space 2} .2792227{col 37}{space 1}    6.00{col 46}{space 3}0.000{col 54}{space 4} 1.128238{col 67}{space 3} 2.222771
{txt}{space 9}27  {c |}{col 14}{res}{space 2} 2.608073{col 26}{space 2}  .242953{col 37}{space 1}   10.73{col 46}{space 3}0.000{col 54}{space 4} 2.131893{col 67}{space 3} 3.084252
{txt}{space 9}28  {c |}{col 14}{res}{space 2} 2.156392{col 26}{space 2} .2449986{col 37}{space 1}    8.80{col 46}{space 3}0.000{col 54}{space 4} 1.676204{col 67}{space 3}  2.63658
{txt}{space 9}29  {c |}{col 14}{res}{space 2} 2.011519{col 26}{space 2} .2377849{col 37}{space 1}    8.46{col 46}{space 3}0.000{col 54}{space 4} 1.545469{col 67}{space 3} 2.477569
{txt}{space 9}30  {c |}{col 14}{res}{space 2} 1.692342{col 26}{space 2} .2598011{col 37}{space 1}    6.51{col 46}{space 3}0.000{col 54}{space 4} 1.183141{col 67}{space 3} 2.201543
{txt}{space 9}31  {c |}{col 14}{res}{space 2} 1.953733{col 26}{space 2} .2446281{col 37}{space 1}    7.99{col 46}{space 3}0.000{col 54}{space 4}  1.47427{col 67}{space 3} 2.433195
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-3.479732{col 26}{space 2} .4088892{col 37}{space 1}   -8.51{col 46}{space 3}0.000{col 54}{space 4} -4.28114{col 67}{space 3}-2.678324
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict green2logit2
{txt}(option {bf:pr} assumed; Pr(green2))
(11,197 missing values generated)

{com}. 
. *4. With quadratic terms
. 
. logit PS2 education edu2 agea agea2 female bebe minority y y2 uemp3m i.source polintr polintr2 i.election

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-22842.308}  
Iteration 1:{space 3}log likelihood = {res: -22017.57}  
Iteration 2:{space 3}log likelihood = {res:-21993.161}  
Iteration 3:{space 3}log likelihood = {res:-21993.013}  
Iteration 4:{space 3}log likelihood = {res:-21993.013}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    40,640
{txt}{col 49}LR chi2({res}44{txt}){col 67}= {res}   1698.59
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-21993.013{txt}{col 49}Pseudo R2{col 67}= {res}    0.0372

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         PS2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2}-.1383569{col 26}{space 2} .0333231{col 37}{space 1}   -4.15{col 46}{space 3}0.000{col 54}{space 4}-.2036689{col 67}{space 3}-.0730449
{txt}{space 8}edu2 {c |}{col 14}{res}{space 2} .0057438{col 26}{space 2} .0072133{col 37}{space 1}    0.80{col 46}{space 3}0.426{col 54}{space 4} -.008394{col 67}{space 3} .0198817
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0211535{col 26}{space 2}  .004202{col 37}{space 1}    5.03{col 46}{space 3}0.000{col 54}{space 4} .0129178{col 67}{space 3} .0293891
{txt}{space 7}agea2 {c |}{col 14}{res}{space 2}-.0001873{col 26}{space 2} .0000418{col 37}{space 1}   -4.48{col 46}{space 3}0.000{col 54}{space 4}-.0002691{col 67}{space 3}-.0001054
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0507812{col 26}{space 2} .0239054{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .0039275{col 67}{space 3} .0976349
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0251297{col 26}{space 2} .0272111{col 37}{space 1}    0.92{col 46}{space 3}0.356{col 54}{space 4} -.028203{col 67}{space 3} .0784624
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.5937748{col 26}{space 2} .0690811{col 37}{space 1}   -8.60{col 46}{space 3}0.000{col 54}{space 4}-.7291714{col 67}{space 3}-.4583783
{txt}{space 11}y {c |}{col 14}{res}{space 2} .2750571{col 26}{space 2} .0667516{col 37}{space 1}    4.12{col 46}{space 3}0.000{col 54}{space 4} .1442264{col 67}{space 3} .4058877
{txt}{space 10}y2 {c |}{col 14}{res}{space 2}-.0546494{col 26}{space 2} .0152164{col 37}{space 1}   -3.59{col 46}{space 3}0.000{col 54}{space 4} -.084473{col 67}{space 3}-.0248258
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.1382941{col 26}{space 2} .0279448{col 37}{space 1}   -4.95{col 46}{space 3}0.000{col 54}{space 4}-.1930649{col 67}{space 3}-.0835234
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5325115{col 26}{space 2}  .044142{col 37}{space 1}   12.06{col 46}{space 3}0.000{col 54}{space 4} .4459948{col 67}{space 3} .6190282
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .5486649{col 26}{space 2} .0739687{col 37}{space 1}    7.42{col 46}{space 3}0.000{col 54}{space 4} .4036888{col 67}{space 3} .6936409
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .578248{col 26}{space 2} .0526979{col 37}{space 1}   10.97{col 46}{space 3}0.000{col 54}{space 4}  .474962{col 67}{space 3}  .681534
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.0167021{col 26}{space 2} .0671497{col 37}{space 1}   -0.25{col 46}{space 3}0.804{col 54}{space 4}-.1483132{col 67}{space 3} .1149089
{txt}{space 4}polintr2 {c |}{col 14}{res}{space 2}-.0149305{col 26}{space 2}   .01324{col 37}{space 1}   -1.13{col 46}{space 3}0.259{col 54}{space 4}-.0408804{col 67}{space 3} .0110194
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.2687934{col 26}{space 2} .1157448{col 37}{space 1}   -2.32{col 46}{space 3}0.020{col 54}{space 4}-.4956491{col 67}{space 3}-.0419377
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.5018643{col 26}{space 2} .0939432{col 37}{space 1}   -5.34{col 46}{space 3}0.000{col 54}{space 4}-.6859896{col 67}{space 3}-.3177391
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5672286{col 26}{space 2}  .082183{col 37}{space 1}    6.90{col 46}{space 3}0.000{col 54}{space 4} .4061529{col 67}{space 3} .7283044
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .6429695{col 26}{space 2} .0821553{col 37}{space 1}    7.83{col 46}{space 3}0.000{col 54}{space 4} .4819482{col 67}{space 3} .8039909
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.4097707{col 26}{space 2} .0906349{col 37}{space 1}   -4.52{col 46}{space 3}0.000{col 54}{space 4}-.5874119{col 67}{space 3}-.2321296
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-1.343112{col 26}{space 2} .1204913{col 37}{space 1}  -11.15{col 46}{space 3}0.000{col 54}{space 4} -1.57927{col 67}{space 3}-1.106953
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.4037223{col 26}{space 2} .1108206{col 37}{space 1}   -3.64{col 46}{space 3}0.000{col 54}{space 4}-.6209267{col 67}{space 3}-.1865179
{txt}{space 10}9  {c |}{col 14}{res}{space 2}  .154034{col 26}{space 2} .0871526{col 37}{space 1}    1.77{col 46}{space 3}0.077{col 54}{space 4} -.016782{col 67}{space 3}   .32485
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0041314{col 26}{space 2} .0919138{col 37}{space 1}   -0.04{col 46}{space 3}0.964{col 54}{space 4}-.1842791{col 67}{space 3} .1760164
{txt}{space 9}11  {c |}{col 14}{res}{space 2} -.157245{col 26}{space 2} .0819984{col 37}{space 1}   -1.92{col 46}{space 3}0.055{col 54}{space 4}-.3179589{col 67}{space 3} .0034689
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1309043{col 26}{space 2} .0870064{col 37}{space 1}    1.50{col 46}{space 3}0.132{col 54}{space 4}-.0396251{col 67}{space 3} .3014336
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3246697{col 26}{space 2} .0894481{col 37}{space 1}    3.63{col 46}{space 3}0.000{col 54}{space 4} .1493547{col 67}{space 3} .4999848
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.2153544{col 26}{space 2} .0921596{col 37}{space 1}   -2.34{col 46}{space 3}0.019{col 54}{space 4}-.3959838{col 67}{space 3} -.034725
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.5719949{col 26}{space 2} .1073775{col 37}{space 1}   -5.33{col 46}{space 3}0.000{col 54}{space 4}-.7824509{col 67}{space 3}-.3615389
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3633628{col 26}{space 2} .0840009{col 37}{space 1}    4.33{col 46}{space 3}0.000{col 54}{space 4}  .198724{col 67}{space 3} .5280015
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .0549735{col 26}{space 2} .0855205{col 37}{space 1}    0.64{col 46}{space 3}0.520{col 54}{space 4}-.1126436{col 67}{space 3} .2225906
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2936643{col 26}{space 2} .0883196{col 37}{space 1}    3.33{col 46}{space 3}0.001{col 54}{space 4} .1205612{col 67}{space 3} .4667675
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .0055087{col 26}{space 2} .0879182{col 37}{space 1}    0.06{col 46}{space 3}0.950{col 54}{space 4}-.1668079{col 67}{space 3} .1778252
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.1473307{col 26}{space 2} .0947308{col 37}{space 1}   -1.56{col 46}{space 3}0.120{col 54}{space 4}-.3329997{col 67}{space 3} .0383384
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.6220634{col 26}{space 2} .0918973{col 37}{space 1}   -6.77{col 46}{space 3}0.000{col 54}{space 4}-.8021788{col 67}{space 3}-.4419481
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.8327161{col 26}{space 2} .0974492{col 37}{space 1}   -8.55{col 46}{space 3}0.000{col 54}{space 4}-1.023713{col 67}{space 3}-.6417192
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.1883018{col 26}{space 2} .0891836{col 37}{space 1}   -2.11{col 46}{space 3}0.035{col 54}{space 4}-.3630986{col 67}{space 3}-.0135051
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .1280862{col 26}{space 2} .1119688{col 37}{space 1}    1.14{col 46}{space 3}0.253{col 54}{space 4}-.0913686{col 67}{space 3}  .347541
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.2630216{col 26}{space 2} .0981739{col 37}{space 1}   -2.68{col 46}{space 3}0.007{col 54}{space 4}-.4554388{col 67}{space 3}-.0706044
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .0674957{col 26}{space 2} .0902531{col 37}{space 1}    0.75{col 46}{space 3}0.455{col 54}{space 4}-.1093972{col 67}{space 3} .2443886
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.5286337{col 26}{space 2} .0973446{col 37}{space 1}   -5.43{col 46}{space 3}0.000{col 54}{space 4}-.7194257{col 67}{space 3}-.3378418
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.0819015{col 26}{space 2}  .079843{col 37}{space 1}   -1.03{col 46}{space 3}0.305{col 54}{space 4}-.2383909{col 67}{space 3} .0745879
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1309542{col 26}{space 2} .0919748{col 37}{space 1}    1.42{col 46}{space 3}0.155{col 54}{space 4}-.0493132{col 67}{space 3} .3112216
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .0738877{col 26}{space 2} .0859789{col 37}{space 1}    0.86{col 46}{space 3}0.390{col 54}{space 4}-.0946278{col 67}{space 3} .2424032
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.6631694{col 26}{space 2} .2310472{col 37}{space 1}   -2.87{col 46}{space 3}0.004{col 54}{space 4}-1.116013{col 67}{space 3}-.2103252
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict PS2logit3
{txt}(option {bf:pr} assumed; Pr(PS2))
(2,083 missing values generated)

{com}. 
. logit left2 education edu2 agea agea2 female bebe minority y y2 uemp3m i.source polintr polintr2 i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1561 obs not used

note: 7.election != 0 predicts failure perfectly
      7.election dropped and 1221 obs not used

note: 9.election != 0 predicts failure perfectly
      9.election dropped and 1390 obs not used

note: 15.election != 0 predicts failure perfectly
      15.election dropped and 1319 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1490 obs not used

note: 23.election != 0 predicts failure perfectly
      23.election dropped and 1594 obs not used

note: 27.election != 0 predicts failure perfectly
      27.election dropped and 1212 obs not used

note: 31.election != 0 predicts failure perfectly
      31.election dropped and 1455 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -7954.062}  
Iteration 1:{space 3}log likelihood = {res:-7503.6587}  
Iteration 2:{space 3}log likelihood = {res: -7407.472}  
Iteration 3:{space 3}log likelihood = {res:-7405.2622}  
Iteration 4:{space 3}log likelihood = {res:-7405.2298}  
Iteration 5:{space 3}log likelihood = {res:-7405.2298}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,398
{txt}{col 49}LR chi2({res}36{txt}){col 67}= {res}   1097.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7405.2298{txt}{col 49}Pseudo R2{col 67}= {res}    0.0690

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       left2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .0565121{col 26}{space 2}  .072318{col 37}{space 1}    0.78{col 46}{space 3}0.435{col 54}{space 4}-.0852286{col 67}{space 3} .1982528
{txt}{space 8}edu2 {c |}{col 14}{res}{space 2}-.0013668{col 26}{space 2} .0147231{col 37}{space 1}   -0.09{col 46}{space 3}0.926{col 54}{space 4}-.0302236{col 67}{space 3}   .02749
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0103509{col 26}{space 2}  .008644{col 37}{space 1}    1.20{col 46}{space 3}0.231{col 54}{space 4}-.0065911{col 67}{space 3} .0272929
{txt}{space 7}agea2 {c |}{col 14}{res}{space 2}-.0001855{col 26}{space 2} .0000909{col 37}{space 1}   -2.04{col 46}{space 3}0.041{col 54}{space 4}-.0003636{col 67}{space 3}-7.44e-06
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0347928{col 26}{space 2} .0457503{col 37}{space 1}    0.76{col 46}{space 3}0.447{col 54}{space 4}-.0548761{col 67}{space 3} .1244618
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .2116125{col 26}{space 2} .0515936{col 37}{space 1}    4.10{col 46}{space 3}0.000{col 54}{space 4} .1104908{col 67}{space 3} .3127341
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0825471{col 26}{space 2} .1399197{col 37}{space 1}   -0.59{col 46}{space 3}0.555{col 54}{space 4}-.3567847{col 67}{space 3} .1916905
{txt}{space 11}y {c |}{col 14}{res}{space 2} .7187833{col 26}{space 2} .1266972{col 37}{space 1}    5.67{col 46}{space 3}0.000{col 54}{space 4} .4704614{col 67}{space 3} .9671052
{txt}{space 10}y2 {c |}{col 14}{res}{space 2}-.1145314{col 26}{space 2} .0281441{col 37}{space 1}   -4.07{col 46}{space 3}0.000{col 54}{space 4}-.1696928{col 67}{space 3}  -.05937
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.3572547{col 26}{space 2} .0495225{col 37}{space 1}   -7.21{col 46}{space 3}0.000{col 54}{space 4}-.4543169{col 67}{space 3}-.2601924
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3817119{col 26}{space 2} .0805715{col 37}{space 1}    4.74{col 46}{space 3}0.000{col 54}{space 4} .2237948{col 67}{space 3} .5396291
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .6612015{col 26}{space 2} .1272109{col 37}{space 1}    5.20{col 46}{space 3}0.000{col 54}{space 4} .4118727{col 67}{space 3} .9105302
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2044582{col 26}{space 2} .1033903{col 37}{space 1}    1.98{col 46}{space 3}0.048{col 54}{space 4}  .001817{col 67}{space 3} .4070994
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.3806493{col 26}{space 2} .1255072{col 37}{space 1}   -3.03{col 46}{space 3}0.002{col 54}{space 4}-.6266389{col 67}{space 3}-.1346597
{txt}{space 4}polintr2 {c |}{col 14}{res}{space 2} .0284852{col 26}{space 2} .0256815{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0218496{col 67}{space 3} .0788199
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-2.293664{col 26}{space 2} .3465804{col 37}{space 1}   -6.62{col 46}{space 3}0.000{col 54}{space 4}-2.972949{col 67}{space 3}-1.614379
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.5030795{col 26}{space 2} .1245846{col 37}{space 1}   -4.04{col 46}{space 3}0.000{col 54}{space 4}-.7472607{col 67}{space 3}-.2588982
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}-1.389565{col 26}{space 2} .1585255{col 37}{space 1}   -8.77{col 46}{space 3}0.000{col 54}{space 4}-1.700269{col 67}{space 3}-1.078861
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-1.046123{col 26}{space 2} .1389805{col 37}{space 1}   -7.53{col 46}{space 3}0.000{col 54}{space 4} -1.31852{col 67}{space 3}-.7737261
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}8  {c |}{col 14}{res}{space 2}-1.110897{col 26}{space 2} .1866254{col 37}{space 1}   -5.95{col 46}{space 3}0.000{col 54}{space 4}-1.476676{col 67}{space 3}-.7451175
{txt}{space 10}9  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}10  {c |}{col 14}{res}{space 2} -1.58266{col 26}{space 2} .1934317{col 37}{space 1}   -8.18{col 46}{space 3}0.000{col 54}{space 4}-1.961779{col 67}{space 3} -1.20354
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.6120933{col 26}{space 2} .1135223{col 37}{space 1}   -5.39{col 46}{space 3}0.000{col 54}{space 4} -.834593{col 67}{space 3}-.3895937
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-1.247533{col 26}{space 2} .1533537{col 37}{space 1}   -8.13{col 46}{space 3}0.000{col 54}{space 4}  -1.5481{col 67}{space 3}-.9469648
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.0359731{col 26}{space 2} .1217067{col 37}{space 1}   -0.30{col 46}{space 3}0.768{col 54}{space 4}-.2745139{col 67}{space 3} .2025676
{txt}{space 9}15  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}16  {c |}{col 14}{res}{space 2}-2.930674{col 26}{space 2} .3884582{col 37}{space 1}   -7.54{col 46}{space 3}0.000{col 54}{space 4}-3.692039{col 67}{space 3} -2.16931
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-1.104073{col 26}{space 2} .1474267{col 37}{space 1}   -7.49{col 46}{space 3}0.000{col 54}{space 4}-1.393024{col 67}{space 3}-.8151223
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2}-.4446352{col 26}{space 2} .1300137{col 37}{space 1}   -3.42{col 46}{space 3}0.001{col 54}{space 4}-.6994574{col 67}{space 3}-.1898131
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.3833462{col 26}{space 2} .1213386{col 37}{space 1}   -3.16{col 46}{space 3}0.002{col 54}{space 4}-.6211656{col 67}{space 3}-.1455268
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.7753654{col 26}{space 2} .1438441{col 37}{space 1}   -5.39{col 46}{space 3}0.000{col 54}{space 4}-1.057295{col 67}{space 3}-.4934361
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-1.031428{col 26}{space 2} .1334333{col 37}{space 1}   -7.73{col 46}{space 3}0.000{col 54}{space 4}-1.292953{col 67}{space 3}-.7699035
{txt}{space 9}23  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}24  {c |}{col 14}{res}{space 2} -.468065{col 26}{space 2} .1230368{col 37}{space 1}   -3.80{col 46}{space 3}0.000{col 54}{space 4}-.7092128{col 67}{space 3}-.2269173
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-1.273068{col 26}{space 2} .2118629{col 37}{space 1}   -6.01{col 46}{space 3}0.000{col 54}{space 4}-1.688311{col 67}{space 3} -.857824
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-1.254465{col 26}{space 2} .1769726{col 37}{space 1}   -7.09{col 46}{space 3}0.000{col 54}{space 4}-1.601325{col 67}{space 3}-.9076054
{txt}{space 9}27  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}28  {c |}{col 14}{res}{space 2} .4079896{col 26}{space 2} .1082929{col 37}{space 1}    3.77{col 46}{space 3}0.000{col 54}{space 4} .1957394{col 67}{space 3} .6202399
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.3072267{col 26}{space 2} .1050585{col 37}{space 1}   -2.92{col 46}{space 3}0.003{col 54}{space 4}-.5131375{col 67}{space 3}-.1013159
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-1.191414{col 26}{space 2} .1633789{col 37}{space 1}   -7.29{col 46}{space 3}0.000{col 54}{space 4}-1.511631{col 67}{space 3}-.8711976
{txt}{space 9}31  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-2.037835{col 26}{space 2} .4401202{col 37}{space 1}   -4.63{col 46}{space 3}0.000{col 54}{space 4}-2.900455{col 67}{space 3}-1.175215
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict left2logit3
{txt}(option {bf:pr} assumed; Pr(left2))
(17,177 missing values generated)

{com}. 
. logit green2 education edu2 agea agea2 female bebe minority y y2 uemp3m i.source polintr polintr2 i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1561 obs not used

note: 5.election != 0 predicts failure perfectly
      5.election dropped and 1672 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1490 obs not used

note: 21.election != 0 predicts failure perfectly
      21.election dropped and 1161 obs not used

note: 25.election != 0 predicts failure perfectly
      25.election dropped and 584 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-8466.7241}  
Iteration 1:{space 3}log likelihood = {res:-7803.1442}  
Iteration 2:{space 3}log likelihood = {res: -7661.683}  
Iteration 3:{space 3}log likelihood = {res:-7659.3421}  
Iteration 4:{space 3}log likelihood = {res:-7659.3338}  
Iteration 5:{space 3}log likelihood = {res:-7659.3338}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    34,172
{txt}{col 49}LR chi2({res}39{txt}){col 67}= {res}   1614.78
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7659.3338{txt}{col 49}Pseudo R2{col 67}= {res}    0.0954

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      green2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .2258367{col 26}{space 2} .0883027{col 37}{space 1}    2.56{col 46}{space 3}0.011{col 54}{space 4} .0527666{col 67}{space 3} .3989068
{txt}{space 8}edu2 {c |}{col 14}{res}{space 2} .0189221{col 26}{space 2} .0169494{col 37}{space 1}    1.12{col 46}{space 3}0.264{col 54}{space 4}-.0142981{col 67}{space 3} .0521423
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0034992{col 26}{space 2} .0090879{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4}-.0143126{col 67}{space 3} .0213111
{txt}{space 7}agea2 {c |}{col 14}{res}{space 2}-.0002331{col 26}{space 2} .0000984{col 37}{space 1}   -2.37{col 46}{space 3}0.018{col 54}{space 4} -.000426{col 67}{space 3}-.0000403
{txt}{space 6}female {c |}{col 14}{res}{space 2} .2458076{col 26}{space 2} .0454486{col 37}{space 1}    5.41{col 46}{space 3}0.000{col 54}{space 4} .1567298{col 67}{space 3} .3348853
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0441597{col 26}{space 2} .0506122{col 37}{space 1}    0.87{col 46}{space 3}0.383{col 54}{space 4}-.0550383{col 67}{space 3} .1433577
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.1207809{col 26}{space 2} .1363306{col 37}{space 1}   -0.89{col 46}{space 3}0.376{col 54}{space 4} -.387984{col 67}{space 3} .1464223
{txt}{space 11}y {c |}{col 14}{res}{space 2} .0460856{col 26}{space 2} .1324886{col 37}{space 1}    0.35{col 46}{space 3}0.728{col 54}{space 4}-.2135872{col 67}{space 3} .3057584
{txt}{space 10}y2 {c |}{col 14}{res}{space 2}-.0127802{col 26}{space 2} .0318366{col 37}{space 1}   -0.40{col 46}{space 3}0.688{col 54}{space 4}-.0751788{col 67}{space 3} .0496183
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.2256037{col 26}{space 2} .0511099{col 37}{space 1}   -4.41{col 46}{space 3}0.000{col 54}{space 4}-.3257773{col 67}{space 3}-.1254302
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1693722{col 26}{space 2} .0714688{col 37}{space 1}   -2.37{col 46}{space 3}0.018{col 54}{space 4}-.3094484{col 67}{space 3}-.0292959
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2122739{col 26}{space 2}  .124189{col 37}{space 1}    1.71{col 46}{space 3}0.087{col 54}{space 4}-.0311321{col 67}{space 3} .4556799
{txt}{space 10}3  {c |}{col 14}{res}{space 2} -.063948{col 26}{space 2}  .101089{col 37}{space 1}   -0.63{col 46}{space 3}0.527{col 54}{space 4}-.2620789{col 67}{space 3} .1341828
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.3321727{col 26}{space 2} .1273193{col 37}{space 1}   -2.61{col 46}{space 3}0.009{col 54}{space 4}-.5817139{col 67}{space 3}-.0826315
{txt}{space 4}polintr2 {c |}{col 14}{res}{space 2} .0289513{col 26}{space 2} .0267325{col 37}{space 1}    1.08{col 46}{space 3}0.279{col 54}{space 4}-.0234434{col 67}{space 3} .0813461
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.595113{col 26}{space 2} .2871492{col 37}{space 1}    5.55{col 46}{space 3}0.000{col 54}{space 4} 1.032311{col 67}{space 3} 2.157916
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.886548{col 26}{space 2} .2484788{col 37}{space 1}    7.59{col 46}{space 3}0.000{col 54}{space 4} 1.399539{col 67}{space 3} 2.373558
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}6  {c |}{col 14}{res}{space 2} 1.972319{col 26}{space 2} .2456023{col 37}{space 1}    8.03{col 46}{space 3}0.000{col 54}{space 4} 1.490948{col 67}{space 3} 2.453691
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .5701746{col 26}{space 2} .2934368{col 37}{space 1}    1.94{col 46}{space 3}0.052{col 54}{space 4}-.0049509{col 67}{space 3}   1.1453
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .5441822{col 26}{space 2} .3471874{col 37}{space 1}    1.57{col 46}{space 3}0.117{col 54}{space 4}-.1362925{col 67}{space 3} 1.224657
{txt}{space 10}9  {c |}{col 14}{res}{space 2} 2.488925{col 26}{space 2} .2414689{col 37}{space 1}   10.31{col 46}{space 3}0.000{col 54}{space 4} 2.015655{col 67}{space 3} 2.962196
{txt}{space 9}10  {c |}{col 14}{res}{space 2} 1.382494{col 26}{space 2} .2867732{col 37}{space 1}    4.82{col 46}{space 3}0.000{col 54}{space 4} .8204293{col 67}{space 3}  1.94456
{txt}{space 9}11  {c |}{col 14}{res}{space 2} 2.386365{col 26}{space 2} .2367009{col 37}{space 1}   10.08{col 46}{space 3}0.000{col 54}{space 4}  1.92244{col 67}{space 3} 2.850291
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  1.28029{col 26}{space 2} .2622658{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .7662581{col 67}{space 3} 1.794321
{txt}{space 9}13  {c |}{col 14}{res}{space 2} 1.038011{col 26}{space 2} .2760741{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} .4969151{col 67}{space 3} 1.579106
{txt}{space 9}15  {c |}{col 14}{res}{space 2} 2.645119{col 26}{space 2} .2400253{col 37}{space 1}   11.02{col 46}{space 3}0.000{col 54}{space 4} 2.174678{col 67}{space 3}  3.11556
{txt}{space 9}16  {c |}{col 14}{res}{space 2} 1.931555{col 26}{space 2} .2607666{col 37}{space 1}    7.41{col 46}{space 3}0.000{col 54}{space 4} 1.420462{col 67}{space 3} 2.442648
{txt}{space 9}17  {c |}{col 14}{res}{space 2} 1.400289{col 26}{space 2} .2580273{col 37}{space 1}    5.43{col 46}{space 3}0.000{col 54}{space 4} .8945645{col 67}{space 3} 1.906013
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2} .6410207{col 26}{space 2} .2928674{col 37}{space 1}    2.19{col 46}{space 3}0.029{col 54}{space 4} .0670112{col 67}{space 3}  1.21503
{txt}{space 9}20  {c |}{col 14}{res}{space 2} 1.500653{col 26}{space 2} .2540637{col 37}{space 1}    5.91{col 46}{space 3}0.000{col 54}{space 4} 1.002697{col 67}{space 3} 1.998609
{txt}{space 9}21  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}22  {c |}{col 14}{res}{space 2} 2.128074{col 26}{space 2} .2422854{col 37}{space 1}    8.78{col 46}{space 3}0.000{col 54}{space 4} 1.653203{col 67}{space 3} 2.602944
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .4783701{col 26}{space 2} .2914111{col 37}{space 1}    1.64{col 46}{space 3}0.101{col 54}{space 4}-.0927851{col 67}{space 3} 1.049525
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .7455443{col 26}{space 2} .2814449{col 37}{space 1}    2.65{col 46}{space 3}0.008{col 54}{space 4} .1939225{col 67}{space 3} 1.297166
{txt}{space 9}25  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}26  {c |}{col 14}{res}{space 2} 1.670565{col 26}{space 2} .2760864{col 37}{space 1}    6.05{col 46}{space 3}0.000{col 54}{space 4} 1.129445{col 67}{space 3} 2.211684
{txt}{space 9}27  {c |}{col 14}{res}{space 2} 2.607187{col 26}{space 2} .2432341{col 37}{space 1}   10.72{col 46}{space 3}0.000{col 54}{space 4} 2.130457{col 67}{space 3} 3.083917
{txt}{space 9}28  {c |}{col 14}{res}{space 2} 2.145084{col 26}{space 2}  .245043{col 37}{space 1}    8.75{col 46}{space 3}0.000{col 54}{space 4} 1.664808{col 67}{space 3} 2.625359
{txt}{space 9}29  {c |}{col 14}{res}{space 2} 2.024668{col 26}{space 2} .2379981{col 37}{space 1}    8.51{col 46}{space 3}0.000{col 54}{space 4}   1.5582{col 67}{space 3} 2.491135
{txt}{space 9}30  {c |}{col 14}{res}{space 2} 1.713752{col 26}{space 2} .2590586{col 37}{space 1}    6.62{col 46}{space 3}0.000{col 54}{space 4} 1.206006{col 67}{space 3} 2.221497
{txt}{space 9}31  {c |}{col 14}{res}{space 2} 1.980932{col 26}{space 2} .2445294{col 37}{space 1}    8.10{col 46}{space 3}0.000{col 54}{space 4} 1.501663{col 67}{space 3}   2.4602
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-3.848442{col 26}{space 2} .4889189{col 37}{space 1}   -7.87{col 46}{space 3}0.000{col 54}{space 4}-4.806705{col 67}{space 3}-2.890178
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict green2logit3
{txt}(option {bf:pr} assumed; Pr(green2))
(11,197 missing values generated)

{com}. 
. 
. *Create variables to ease the reading of the table
. 
. gen real_PS= PS if voting2==0
{txt}(53,037 missing values generated)

{com}. gen CEM_PS= PS2 if cem_matched==1 
{txt}(54,969 missing values generated)

{com}. gen logit_PS = PS2logit if inclusion==1  
{txt}(53,197 missing values generated)

{com}. gen logitplus_PS = PS2logit2 if inclusion==1  & likely_abstainer==0
{txt}(53,906 missing values generated)

{com}. gen quadratic_PS =  PS2logit3 if inclusion==1
{txt}(53,197 missing values generated)

{com}. 
. *Extreme left parties
. 
. gen real_leftb= left if voting2==0
{txt}(53,037 missing values generated)

{com}. gen CEM_left= left2 if cem_matched==1 
{txt}(54,969 missing values generated)

{com}. gen logit_left = left2logit if inclusion==1  
{txt}(53,836 missing values generated)

{com}. gen logitplus_left = left2logit2 if inclusion==1  & likely_abstainer==0
{txt}(54,425 missing values generated)

{com}. gen quadratic_left =  left2logit3 if inclusion==1
{txt}(53,836 missing values generated)

{com}. 
. *Green parties
. 
. gen real_greenb= green if voting2==0
{txt}(53,037 missing values generated)

{com}. gen CEM_green= green2 if cem_matched==1 
{txt}(54,969 missing values generated)

{com}. gen logit_green = green2logit if inclusion==1  
{txt}(53,637 missing values generated)

{com}. gen logitplus_green = green2logit2 if inclusion==1  & likely_abstainer==0
{txt}(54,263 missing values generated)

{com}. gen quadratic_green =  green2logit3 if inclusion==1
{txt}(53,637 missing values generated)

{com}. 
. 
. *******************************************************************************************
. *Creating variables for Table 7 in Appendix (Potential omitted variable bias)
. *******************************************************************************************
. 
. *A10: Omitted variable bias: add covariates
. 
. * (Social networks)
. 
. 
. rename sclact social1
{res}{txt}
{com}.  
. cem  education agea (24 34 44 54 64) female bebe   minority  y uemp3m social1  source(#0) polintr  election(#0), treatment(voting)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}42802
{txt}Number of matched strata: {res}2080

           {txt}    0      1
      All  {res}11137  44900
{txt}  Matched  {res} 2642   4290
{txt}Unmatched  {res} 8495  40610


{txt}Multivariate L1 distance: {res}.6429934

{txt}Univariate imbalance:

                 L1      mean       min       25%       50%       75%       max
education  {res} 2.1e-15   1.1e-14         0         0         0         0         .
{txt}     agea  {res}  .05848   -.36615         1         1         0        -1        -6
{txt}   female  {res} 5.0e-15   5.1e-15         0         0         0         0         0
{txt}     bebe  {res} 4.5e-15   1.6e-15         0         0         0         0         0
{txt} minority  {res} 1.2e-16   6.7e-16         0         0         0         0         .
{txt}        y  {res} 4.2e-15  -1.2e-14         0         0         0         0         .
{txt}   uemp3m  {res} 2.8e-15   4.4e-16         0         0         0         0         .
{txt}  social1  {res} 3.6e-15  -4.0e-14         0         0         0         0         .
{txt}   source  {res} 4.3e-15  -2.4e-14         0         0         0         0         .
{txt}  polintr  {res} 2.0e-15  -2.5e-14         0         0         0         0         0
{txt} election  {res} 3.4e-15  -9.6e-14         0         0         0         0         0
{txt}
{com}. gen cem_social=cem_matched
{txt}
{com}. cem  education agea (24 34 44 54 64) female bebe   minority  y uemp3m source(#0) polintr trstprl    election(#0), treatment(voting)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}47934
{txt}Number of matched strata: {res}1364

           {txt}    0      1
      All  {res}11137  44900
{txt}  Matched  {res} 1640   2327
{txt}Unmatched  {res} 9497  42573


{txt}Multivariate L1 distance: {res}.63285411

{txt}Univariate imbalance:

                 L1      mean       min       25%       50%       75%       max
education  {res} 1.9e-15  -1.2e-14         0         0         0         0         .
{txt}     agea  {res}  .07428   -.62151         1         0         0        -2         2
{txt}   female  {res} 5.3e-15  -6.7e-16         0         0         0         0         0
{txt}     bebe  {res} 3.6e-15  -2.2e-15         0         0         0         0         0
{txt} minority  {res} 5.6e-17         0         0         0         0         0         .
{txt}        y  {res} 4.6e-15   8.0e-15         0         0         0         0         .
{txt}   uemp3m  {res} 2.0e-15  -1.3e-15         0         0         0         0         .
{txt}   source  {res} 4.7e-15   1.9e-14         0         0         0         0         .
{txt}  polintr  {res} 1.5e-15   2.0e-14         0         0         0         0         0
{txt}  trstprl  {res} 4.1e-16   1.7e-14         0         0         0         0         .
{txt} election  {res} 4.3e-16  -3.7e-14         0         0         0         0         0
{txt}
{com}. gen cem_trust=cem_matched
{txt}
{com}. cem  education agea (24 34 44 54 64) female bebe   minority  y uemp3m source(#0) polintr  lrscale  election(#0), treatment(voting)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}47448
{txt}Number of matched strata: {res}1463

           {txt}    0      1
      All  {res}11137  44900
{txt}  Matched  {res} 1861   2633
{txt}Unmatched  {res} 9276  42267


{txt}Multivariate L1 distance: {res}.61606071

{txt}Univariate imbalance:

                 L1      mean       min       25%       50%       75%       max
education  {res} 2.4e-15  -1.1e-14         0         0         0         0         .
{txt}     agea  {res}   .0702   -.43827         3         0        -1        -2        -6
{txt}   female  {res} 4.1e-15  -1.5e-14         0         0         0         0         0
{txt}     bebe  {res} 2.6e-15  -1.4e-14         0         0         0         0         0
{txt} minority  {res} 8.4e-18  -4.4e-16         0         0         0         0         .
{txt}        y  {res} 3.7e-15  -1.9e-14         0         0         0         0         .
{txt}   uemp3m  {res} 1.3e-15  -1.0e-14         0         0         0         0         .
{txt}   source  {res} 4.9e-15  -3.8e-14         0         0         0         0         .
{txt}  polintr  {res} 2.3e-15  -2.2e-15         0         0         0         0         .
{txt}  lrscale  {res} 4.2e-15  -5.3e-15         0         0         0         0         .
{txt} election  {res} 2.8e-15  -6.0e-14         0         0         0         0         0
{txt}
{com}. gen cem_ideology=cem_matched
{txt}
{com}. 
. gen PScem_social = PS if cem_basic==1
{txt}(26,211 missing values generated)

{com}. gen PScem_trust = PS if cem_trust==1
{txt}(53,710 missing values generated)

{com}. gen PScem_ideology = PS if cem_ideology==1
{txt}(53,404 missing values generated)

{com}. 
. gen leftcem_social = left if cem_social ==1
{txt}(51,747 missing values generated)

{com}. gen leftcem_trust = left if cem_trust ==1
{txt}(53,710 missing values generated)

{com}. gen leftcem_ideology= left if cem_ideology ==1
{txt}(53,404 missing values generated)

{com}. 
. gen greencem_social = green if cem_social ==1
{txt}(51,747 missing values generated)

{com}. gen greencem_trust = green if cem_trust ==1
{txt}(53,710 missing values generated)

{com}. gen greencem_ideology= green if cem_ideology ==1
{txt}(53,404 missing values generated)

{com}. 
. *2nd step: compute imputation for Logit and logit plus, for each party  (PS, left, green) and each specification
. 
. *Logit 
. 
. *social
. quietly: logit PS   education agea female bebe minority   y uemp3m  i.source polintr social1   i.election  
{txt}
{com}. predict PSlogit_social if voting==0
{txt}(option {bf:pr} assumed; Pr(PS))
(45,745 missing values generated)

{com}. quietly: logit left   education agea female bebe minority   y uemp3m  i.source polintr social1   i.election    
{txt}
{com}. predict leftlogit_social if voting==0
{txt}(option {bf:pr} assumed; Pr(left))
(48,922 missing values generated)

{com}. quietly: logit green   education agea female bebe minority   y uemp3m  i.source polintr social1   i.election    
{txt}
{com}. predict greenlogit_social if voting==0
{txt}(option {bf:pr} assumed; Pr(green))
(47,930 missing values generated)

{com}. *trust
. quietly: logit PS   education agea female bebe minority   y uemp3m  i.source polintr trstprl   i.election  
{txt}
{com}. predict PSlogit_trust if voting==0
{txt}(option {bf:pr} assumed; Pr(PS))
(46,053 missing values generated)

{com}. quietly: logit left   education agea female bebe minority   y uemp3m  i.source polintr trstprl   i.election    
{txt}
{com}. predict leftlogit_trust if voting==0
{txt}(option {bf:pr} assumed; Pr(left))
(49,116 missing values generated)

{com}. quietly: logit green   education agea female bebe minority   y uemp3m  i.source polintr trstprl   i.election
{txt}
{com}. predict greenlogit_trust if voting==0  
{txt}(option {bf:pr} assumed; Pr(green))
(48,105 missing values generated)

{com}. *ideology
. logit PS   education agea female bebe minority   y uemp3m  i.source polintr  lrscale     i.election  

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-23104.779}  
Iteration 1:{space 3}log likelihood = {res:-20440.264}  
Iteration 2:{space 3}log likelihood = {res:-20324.231}  
Iteration 3:{space 3}log likelihood = {res:-20323.928}  
Iteration 4:{space 3}log likelihood = {res:-20323.928}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    40,204
{txt}{col 49}LR chi2({res}41{txt}){col 67}= {res}   5561.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-20323.928{txt}{col 49}Pseudo R2{col 67}= {res}    0.1204

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PS{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2}-.1358047{col 26}{space 2} .0106485{col 37}{space 1}  -12.75{col 46}{space 3}0.000{col 54}{space 4}-.1566753{col 67}{space 3}-.1149341
{txt}{space 8}agea {c |}{col 14}{res}{space 2} .0058428{col 26}{space 2} .0010199{col 37}{space 1}    5.73{col 46}{space 3}0.000{col 54}{space 4} .0038438{col 67}{space 3} .0078418
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0198183{col 26}{space 2} .0248901{col 37}{space 1}    0.80{col 46}{space 3}0.426{col 54}{space 4}-.0289654{col 67}{space 3} .0686021
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}-.0355555{col 26}{space 2} .0275588{col 37}{space 1}   -1.29{col 46}{space 3}0.197{col 54}{space 4}-.0895698{col 67}{space 3} .0184589
{txt}{space 4}minority {c |}{col 14}{res}{space 2} -.532681{col 26}{space 2} .0723276{col 37}{space 1}   -7.36{col 46}{space 3}0.000{col 54}{space 4}-.6744405{col 67}{space 3}-.3909215
{txt}{space 11}y {c |}{col 14}{res}{space 2}-.0077053{col 26}{space 2} .0178941{col 37}{space 1}   -0.43{col 46}{space 3}0.667{col 54}{space 4}-.0427771{col 67}{space 3} .0273664
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.0239495{col 26}{space 2} .0288586{col 37}{space 1}   -0.83{col 46}{space 3}0.407{col 54}{space 4}-.0805112{col 67}{space 3} .0326123
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  .420398{col 26}{space 2} .0457391{col 37}{space 1}    9.19{col 46}{space 3}0.000{col 54}{space 4}  .330751{col 67}{space 3}  .510045
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3117101{col 26}{space 2} .0737401{col 37}{space 1}    4.23{col 46}{space 3}0.000{col 54}{space 4} .1671822{col 67}{space 3} .4562381
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4585797{col 26}{space 2} .0536949{col 37}{space 1}    8.54{col 46}{space 3}0.000{col 54}{space 4} .3533397{col 67}{space 3} .5638198
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.0016697{col 26}{space 2} .0152447{col 37}{space 1}   -0.11{col 46}{space 3}0.913{col 54}{space 4}-.0315488{col 67}{space 3} .0282094
{txt}{space 5}lrscale {c |}{col 14}{res}{space 2}-.3645292{col 26}{space 2} .0064537{col 37}{space 1}  -56.48{col 46}{space 3}0.000{col 54}{space 4}-.3771782{col 67}{space 3}-.3518801
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.4497048{col 26}{space 2} .1267995{col 37}{space 1}   -3.55{col 46}{space 3}0.000{col 54}{space 4}-.6982272{col 67}{space 3}-.2011823
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.5392535{col 26}{space 2} .0977179{col 37}{space 1}   -5.52{col 46}{space 3}0.000{col 54}{space 4} -.730777{col 67}{space 3}  -.34773
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5759872{col 26}{space 2} .0863921{col 37}{space 1}    6.67{col 46}{space 3}0.000{col 54}{space 4} .4066617{col 67}{space 3} .7453127
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .5846356{col 26}{space 2} .0864084{col 37}{space 1}    6.77{col 46}{space 3}0.000{col 54}{space 4} .4152783{col 67}{space 3} .7539929
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.2712713{col 26}{space 2}  .094653{col 37}{space 1}   -2.87{col 46}{space 3}0.004{col 54}{space 4}-.4567877{col 67}{space 3}-.0857548
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-1.354603{col 26}{space 2} .1226221{col 37}{space 1}  -11.05{col 46}{space 3}0.000{col 54}{space 4}-1.594938{col 67}{space 3}-1.114268
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.5752407{col 26}{space 2} .1176237{col 37}{space 1}   -4.89{col 46}{space 3}0.000{col 54}{space 4} -.805779{col 67}{space 3}-.3447024
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0096922{col 26}{space 2} .0914973{col 37}{space 1}    0.11{col 46}{space 3}0.916{col 54}{space 4}-.1696392{col 67}{space 3} .1890236
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0902271{col 26}{space 2}  .095987{col 37}{space 1}    0.94{col 46}{space 3}0.347{col 54}{space 4} -.097904{col 67}{space 3} .2783582
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.3925884{col 26}{space 2} .0850547{col 37}{space 1}   -4.62{col 46}{space 3}0.000{col 54}{space 4}-.5592926{col 67}{space 3}-.2258842
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0022651{col 26}{space 2} .0917737{col 37}{space 1}    0.02{col 46}{space 3}0.980{col 54}{space 4}-.1776079{col 67}{space 3} .1821382
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .4237377{col 26}{space 2} .0923483{col 37}{space 1}    4.59{col 46}{space 3}0.000{col 54}{space 4} .2427384{col 67}{space 3}  .604737
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.2803638{col 26}{space 2} .0945666{col 37}{space 1}   -2.96{col 46}{space 3}0.003{col 54}{space 4}-.4657109{col 67}{space 3}-.0950168
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.6441443{col 26}{space 2} .1141212{col 37}{space 1}   -5.64{col 46}{space 3}0.000{col 54}{space 4}-.8678177{col 67}{space 3} -.420471
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3170084{col 26}{space 2} .0886908{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .1431776{col 67}{space 3} .4908392
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .0141647{col 26}{space 2}  .090254{col 37}{space 1}    0.16{col 46}{space 3}0.875{col 54}{space 4}-.1627298{col 67}{space 3} .1910592
{txt}{space 9}19  {c |}{col 14}{res}{space 2}  .375641{col 26}{space 2}  .090817{col 37}{space 1}    4.14{col 46}{space 3}0.000{col 54}{space 4} .1976429{col 67}{space 3} .5536391
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.0204915{col 26}{space 2}  .093392{col 37}{space 1}   -0.22{col 46}{space 3}0.826{col 54}{space 4}-.2035364{col 67}{space 3} .1625534
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.4762657{col 26}{space 2} .0981701{col 37}{space 1}   -4.85{col 46}{space 3}0.000{col 54}{space 4}-.6686756{col 67}{space 3}-.2838559
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.4914082{col 26}{space 2} .0953239{col 37}{space 1}   -5.16{col 46}{space 3}0.000{col 54}{space 4}-.6782396{col 67}{space 3}-.3045767
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.8958584{col 26}{space 2} .1014257{col 37}{space 1}   -8.83{col 46}{space 3}0.000{col 54}{space 4}-1.094649{col 67}{space 3}-.6970677
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.1914889{col 26}{space 2} .0930695{col 37}{space 1}   -2.06{col 46}{space 3}0.040{col 54}{space 4}-.3739018{col 67}{space 3} -.009076
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-.1407666{col 26}{space 2} .1198796{col 37}{space 1}   -1.17{col 46}{space 3}0.240{col 54}{space 4}-.3757262{col 67}{space 3} .0941931
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.1598564{col 26}{space 2} .1003503{col 37}{space 1}   -1.59{col 46}{space 3}0.111{col 54}{space 4}-.3565394{col 67}{space 3} .0368266
{txt}{space 9}27  {c |}{col 14}{res}{space 2}-.1044405{col 26}{space 2} .0939693{col 37}{space 1}   -1.11{col 46}{space 3}0.266{col 54}{space 4}-.2886169{col 67}{space 3} .0797359
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.6886354{col 26}{space 2} .1003622{col 37}{space 1}   -6.86{col 46}{space 3}0.000{col 54}{space 4}-.8853416{col 67}{space 3}-.4919292
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.3307128{col 26}{space 2} .0834038{col 37}{space 1}   -3.97{col 46}{space 3}0.000{col 54}{space 4}-.4941813{col 67}{space 3}-.1672443
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .0171803{col 26}{space 2} .0971098{col 37}{space 1}    0.18{col 46}{space 3}0.860{col 54}{space 4}-.1731513{col 67}{space 3} .2075119
{txt}{space 9}31  {c |}{col 14}{res}{space 2}-.0367196{col 26}{space 2} .0909035{col 37}{space 1}   -0.40{col 46}{space 3}0.686{col 54}{space 4}-.2148871{col 67}{space 3}  .141448
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 1.548763{col 26}{space 2} .1951845{col 37}{space 1}    7.93{col 46}{space 3}0.000{col 54}{space 4} 1.166209{col 67}{space 3} 1.931318
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict PSlogit_ideology if voting==0
{txt}(option {bf:pr} assumed; Pr(PS))
(47,833 missing values generated)

{com}. logit left   education agea female bebe minority   y uemp3m  i.source polintr   lrscale      i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1484 obs not used

note: 7.election != 0 predicts failure perfectly
      7.election dropped and 1199 obs not used

note: 9.election != 0 predicts failure perfectly
      9.election dropped and 1333 obs not used

note: 15.election != 0 predicts failure perfectly
      15.election dropped and 1356 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1432 obs not used

note: 23.election != 0 predicts failure perfectly
      23.election dropped and 1591 obs not used

note: 27.election != 0 predicts failure perfectly
      27.election dropped and 1204 obs not used

note: 31.election != 0 predicts failure perfectly
      31.election dropped and 1457 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-8262.0021}  
Iteration 1:{space 3}log likelihood = {res:-6941.4301}  
Iteration 2:{space 3}log likelihood = {res:-6534.1607}  
Iteration 3:{space 3}log likelihood = {res:-6522.6819}  
Iteration 4:{space 3}log likelihood = {res:-6522.5573}  
Iteration 5:{space 3}log likelihood = {res:-6522.5571}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    29,148
{txt}{col 49}LR chi2({res}33{txt}){col 67}= {res}   3478.89
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-6522.5571{txt}{col 49}Pseudo R2{col 67}= {res}    0.2105

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        left{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .0367933{col 26}{space 2} .0206695{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0037182{col 67}{space 3} .0773047
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0076996{col 26}{space 2} .0018861{col 37}{space 1}   -4.08{col 46}{space 3}0.000{col 54}{space 4}-.0113962{col 67}{space 3} -.004003
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0966848{col 26}{space 2} .0474675{col 37}{space 1}   -2.04{col 46}{space 3}0.042{col 54}{space 4}-.1897194{col 67}{space 3}-.0036503
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .1158566{col 26}{space 2} .0514103{col 37}{space 1}    2.25{col 46}{space 3}0.024{col 54}{space 4} .0150942{col 67}{space 3}  .216619
{txt}{space 4}minority {c |}{col 14}{res}{space 2}   .06586{col 26}{space 2} .1455895{col 37}{space 1}    0.45{col 46}{space 3}0.651{col 54}{space 4}-.2194902{col 67}{space 3} .3512102
{txt}{space 11}y {c |}{col 14}{res}{space 2} .1478735{col 26}{space 2} .0334248{col 37}{space 1}    4.42{col 46}{space 3}0.000{col 54}{space 4}  .082362{col 67}{space 3} .2133849
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.2417608{col 26}{space 2} .0507901{col 37}{space 1}   -4.76{col 46}{space 3}0.000{col 54}{space 4}-.3413075{col 67}{space 3} -.142214
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2039543{col 26}{space 2} .0839414{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4} .0394322{col 67}{space 3} .3684763
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .256923{col 26}{space 2} .1300915{col 37}{space 1}    1.97{col 46}{space 3}0.048{col 54}{space 4} .0019484{col 67}{space 3} .5118976
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0598866{col 26}{space 2} .1036369{col 37}{space 1}    0.58{col 46}{space 3}0.563{col 54}{space 4} -.143238{col 67}{space 3} .2630112
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.0673187{col 26}{space 2} .0296613{col 37}{space 1}   -2.27{col 46}{space 3}0.023{col 54}{space 4}-.1254538{col 67}{space 3}-.0091836
{txt}{space 5}lrscale {c |}{col 14}{res}{space 2} -.555466{col 26}{space 2} .0126505{col 37}{space 1}  -43.91{col 46}{space 3}0.000{col 54}{space 4}-.5802605{col 67}{space 3}-.5306716
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-2.636935{col 26}{space 2} .3550292{col 37}{space 1}   -7.43{col 46}{space 3}0.000{col 54}{space 4} -3.33278{col 67}{space 3}-1.941091
{txt}{space 10}3  {c |}{col 14}{res}{space 2} -.505207{col 26}{space 2}  .132383{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.7646729{col 67}{space 3}-.2457412
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}-1.924813{col 26}{space 2} .1648284{col 37}{space 1}  -11.68{col 46}{space 3}0.000{col 54}{space 4}-2.247871{col 67}{space 3}-1.601756
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.8074572{col 26}{space 2} .1436836{col 37}{space 1}   -5.62{col 46}{space 3}0.000{col 54}{space 4}-1.089072{col 67}{space 3}-.5258424
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}8  {c |}{col 14}{res}{space 2}-1.555422{col 26}{space 2}  .195349{col 37}{space 1}   -7.96{col 46}{space 3}0.000{col 54}{space 4}-1.938299{col 67}{space 3}-1.172545
{txt}{space 10}9  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}10  {c |}{col 14}{res}{space 2}-1.696364{col 26}{space 2} .1898766{col 37}{space 1}   -8.93{col 46}{space 3}0.000{col 54}{space 4}-2.068515{col 67}{space 3}-1.324212
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.8214819{col 26}{space 2} .1179325{col 37}{space 1}   -6.97{col 46}{space 3}0.000{col 54}{space 4}-1.052625{col 67}{space 3}-.5903384
{txt}{space 9}12  {c |}{col 14}{res}{space 2} -1.75288{col 26}{space 2} .1607364{col 37}{space 1}  -10.91{col 46}{space 3}0.000{col 54}{space 4}-2.067918{col 67}{space 3}-1.437843
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.0945691{col 26}{space 2} .1287705{col 37}{space 1}   -0.73{col 46}{space 3}0.463{col 54}{space 4}-.3469546{col 67}{space 3} .1578164
{txt}{space 9}15  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}16  {c |}{col 14}{res}{space 2}-3.242435{col 26}{space 2} .3947237{col 37}{space 1}   -8.21{col 46}{space 3}0.000{col 54}{space 4}-4.016079{col 67}{space 3}-2.468791
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-1.411615{col 26}{space 2} .1550548{col 37}{space 1}   -9.10{col 46}{space 3}0.000{col 54}{space 4}-1.715516{col 67}{space 3}-1.107713
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2}-.4183719{col 26}{space 2}  .133846{col 37}{space 1}   -3.13{col 46}{space 3}0.002{col 54}{space 4}-.6807053{col 67}{space 3}-.1560385
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.5422865{col 26}{space 2} .1322231{col 37}{space 1}   -4.10{col 46}{space 3}0.000{col 54}{space 4}-.8014391{col 67}{space 3} -.283134
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-1.500609{col 26}{space 2} .1512695{col 37}{space 1}   -9.92{col 46}{space 3}0.000{col 54}{space 4}-1.797092{col 67}{space 3}-1.204127
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.8595205{col 26}{space 2} .1404357{col 37}{space 1}   -6.12{col 46}{space 3}0.000{col 54}{space 4}-1.134769{col 67}{space 3}-.5842716
{txt}{space 9}23  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}24  {c |}{col 14}{res}{space 2}-.5106439{col 26}{space 2} .1315082{col 37}{space 1}   -3.88{col 46}{space 3}0.000{col 54}{space 4}-.7683953{col 67}{space 3}-.2528926
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-2.045774{col 26}{space 2} .2209588{col 37}{space 1}   -9.26{col 46}{space 3}0.000{col 54}{space 4}-2.478846{col 67}{space 3}-1.612703
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-1.630404{col 26}{space 2} .1864335{col 37}{space 1}   -8.75{col 46}{space 3}0.000{col 54}{space 4}-1.995807{col 67}{space 3}-1.265001
{txt}{space 9}27  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}28  {c |}{col 14}{res}{space 2} .4157811{col 26}{space 2} .1159511{col 37}{space 1}    3.59{col 46}{space 3}0.000{col 54}{space 4} .1885211{col 67}{space 3} .6430412
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.6307605{col 26}{space 2} .1122491{col 37}{space 1}   -5.62{col 46}{space 3}0.000{col 54}{space 4}-.8507646{col 67}{space 3}-.4107564
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-1.566167{col 26}{space 2} .1703519{col 37}{space 1}   -9.19{col 46}{space 3}0.000{col 54}{space 4} -1.90005{col 67}{space 3}-1.232283
{txt}{space 9}31  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .9443588{col 26}{space 2} .3660552{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4} .2269037{col 67}{space 3} 1.661814
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict leftlogit_ideology if voting==0
{txt}(option {bf:pr} assumed; Pr(left))
(50,455 missing values generated)

{com}. logit green   education agea female bebe minority   y uemp3m  i.source polintr lrscale     i.election

{txt}note: 4.election != 0 predicts failure perfectly
      4.election dropped and 1484 obs not used

note: 5.election != 0 predicts failure perfectly
      5.election dropped and 1578 obs not used

note: 18.election != 0 predicts failure perfectly
      18.election dropped and 1432 obs not used

note: 21.election != 0 predicts failure perfectly
      21.election dropped and 1240 obs not used

note: 25.election != 0 predicts failure perfectly
      25.election dropped and 570 obs not used

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-8556.9152}  
Iteration 1:{space 3}log likelihood = {res:-7527.7053}  
Iteration 2:{space 3}log likelihood = {res:-7276.4395}  
Iteration 3:{space 3}log likelihood = {res:-7271.3924}  
Iteration 4:{space 3}log likelihood = {res:-7271.3888}  
Iteration 5:{space 3}log likelihood = {res:-7271.3888}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    33,900
{txt}{col 49}LR chi2({res}36{txt}){col 67}= {res}   2571.05
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-7271.3888{txt}{col 49}Pseudo R2{col 67}= {res}    0.1502

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       green{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}education {c |}{col 14}{res}{space 2} .3294828{col 26}{space 2} .0211773{col 37}{space 1}   15.56{col 46}{space 3}0.000{col 54}{space 4}  .287976{col 67}{space 3} .3709895
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0152439{col 26}{space 2} .0018458{col 37}{space 1}   -8.26{col 46}{space 3}0.000{col 54}{space 4}-.0188616{col 67}{space 3}-.0116263
{txt}{space 6}female {c |}{col 14}{res}{space 2} .1857996{col 26}{space 2} .0459884{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4} .0956639{col 67}{space 3} .2759352
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}-.0664997{col 26}{space 2} .0489776{col 37}{space 1}   -1.36{col 46}{space 3}0.175{col 54}{space 4} -.162494{col 67}{space 3} .0294946
{txt}{space 4}minority {c |}{col 14}{res}{space 2} .0065977{col 26}{space 2} .1389078{col 37}{space 1}    0.05{col 46}{space 3}0.962{col 54}{space 4}-.2656567{col 67}{space 3} .2788521
{txt}{space 11}y {c |}{col 14}{res}{space 2}-.0515753{col 26}{space 2} .0342178{col 37}{space 1}   -1.51{col 46}{space 3}0.132{col 54}{space 4}-.1186409{col 67}{space 3} .0154903
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}  -.12033{col 26}{space 2} .0511552{col 37}{space 1}   -2.35{col 46}{space 3}0.019{col 54}{space 4}-.2205924{col 67}{space 3}-.0200677
{txt}{space 12} {c |}
{space 6}source {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2843759{col 26}{space 2} .0738622{col 37}{space 1}   -3.85{col 46}{space 3}0.000{col 54}{space 4}-.4291431{col 67}{space 3}-.1396087
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0376579{col 26}{space 2} .1219196{col 37}{space 1}   -0.31{col 46}{space 3}0.757{col 54}{space 4}-.2766158{col 67}{space 3} .2013001
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2263355{col 26}{space 2}  .098049{col 37}{space 1}   -2.31{col 46}{space 3}0.021{col 54}{space 4} -.418508{col 67}{space 3}-.0341629
{txt}{space 12} {c |}
{space 5}polintr {c |}{col 14}{res}{space 2}-.1187053{col 26}{space 2} .0295903{col 37}{space 1}   -4.01{col 46}{space 3}0.000{col 54}{space 4}-.1767011{col 67}{space 3}-.0607095
{txt}{space 5}lrscale {c |}{col 14}{res}{space 2} -.343777{col 26}{space 2} .0115149{col 37}{space 1}  -29.86{col 46}{space 3}0.000{col 54}{space 4}-.3663457{col 67}{space 3}-.3212083
{txt}{space 12} {c |}
{space 4}election {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.520388{col 26}{space 2} .2971612{col 37}{space 1}    5.12{col 46}{space 3}0.000{col 54}{space 4} .9379626{col 67}{space 3} 2.102813
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  1.99035{col 26}{space 2} .2498993{col 37}{space 1}    7.96{col 46}{space 3}0.000{col 54}{space 4} 1.500556{col 67}{space 3} 2.480144
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}5  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 10}6  {c |}{col 14}{res}{space 2}  2.30306{col 26}{space 2} .2472111{col 37}{space 1}    9.32{col 46}{space 3}0.000{col 54}{space 4} 1.818535{col 67}{space 3} 2.787585
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .7227022{col 26}{space 2} .2933035{col 37}{space 1}    2.46{col 46}{space 3}0.014{col 54}{space 4} .1478378{col 67}{space 3} 1.297566
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2984798{col 26}{space 2} .3641199{col 37}{space 1}    0.82{col 46}{space 3}0.412{col 54}{space 4}-.4151821{col 67}{space 3} 1.012142
{txt}{space 10}9  {c |}{col 14}{res}{space 2} 2.437056{col 26}{space 2} .2433049{col 37}{space 1}   10.02{col 46}{space 3}0.000{col 54}{space 4} 1.960188{col 67}{space 3} 2.913925
{txt}{space 9}10  {c |}{col 14}{res}{space 2} 1.662531{col 26}{space 2} .2816694{col 37}{space 1}    5.90{col 46}{space 3}0.000{col 54}{space 4} 1.110469{col 67}{space 3} 2.214593
{txt}{space 9}11  {c |}{col 14}{res}{space 2} 2.270764{col 26}{space 2} .2378306{col 37}{space 1}    9.55{col 46}{space 3}0.000{col 54}{space 4} 1.804625{col 67}{space 3} 2.736903
{txt}{space 9}12  {c |}{col 14}{res}{space 2} 1.152252{col 26}{space 2} .2632792{col 37}{space 1}    4.38{col 46}{space 3}0.000{col 54}{space 4} .6362339{col 67}{space 3} 1.668269
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .7118506{col 26}{space 2}  .290878{col 37}{space 1}    2.45{col 46}{space 3}0.014{col 54}{space 4} .1417402{col 67}{space 3} 1.281961
{txt}{space 9}15  {c |}{col 14}{res}{space 2} 2.633801{col 26}{space 2}  .241986{col 37}{space 1}   10.88{col 46}{space 3}0.000{col 54}{space 4} 2.159517{col 67}{space 3} 3.108084
{txt}{space 9}16  {c |}{col 14}{res}{space 2} 1.934941{col 26}{space 2} .2653977{col 37}{space 1}    7.29{col 46}{space 3}0.000{col 54}{space 4} 1.414772{col 67}{space 3} 2.455111
{txt}{space 9}17  {c |}{col 14}{res}{space 2} 1.412823{col 26}{space 2}  .259328{col 37}{space 1}    5.45{col 46}{space 3}0.000{col 54}{space 4} .9045491{col 67}{space 3} 1.921096
{txt}{space 9}18  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}19  {c |}{col 14}{res}{space 2} .6198402{col 26}{space 2} .2962682{col 37}{space 1}    2.09{col 46}{space 3}0.036{col 54}{space 4} .0391652{col 67}{space 3} 1.200515
{txt}{space 9}20  {c |}{col 14}{res}{space 2} 1.463918{col 26}{space 2} .2566927{col 37}{space 1}    5.70{col 46}{space 3}0.000{col 54}{space 4} .9608092{col 67}{space 3} 1.967026
{txt}{space 9}21  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}22  {c |}{col 14}{res}{space 2} 2.412241{col 26}{space 2}  .243895{col 37}{space 1}    9.89{col 46}{space 3}0.000{col 54}{space 4} 1.934215{col 67}{space 3} 2.890266
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .7003865{col 26}{space 2} .2867902{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4}  .138288{col 67}{space 3} 1.262485
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .7793979{col 26}{space 2} .2823228{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} .2260553{col 67}{space 3}  1.33274
{txt}{space 9}25  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 9}26  {c |}{col 14}{res}{space 2}  1.73881{col 26}{space 2} .2782657{col 37}{space 1}    6.25{col 46}{space 3}0.000{col 54}{space 4} 1.193419{col 67}{space 3} 2.284201
{txt}{space 9}27  {c |}{col 14}{res}{space 2} 2.522812{col 26}{space 2} .2450767{col 37}{space 1}   10.29{col 46}{space 3}0.000{col 54}{space 4} 2.042471{col 67}{space 3} 3.003154
{txt}{space 9}28  {c |}{col 14}{res}{space 2} 2.185745{col 26}{space 2}  .246182{col 37}{space 1}    8.88{col 46}{space 3}0.000{col 54}{space 4} 1.703237{col 67}{space 3} 2.668253
{txt}{space 9}29  {c |}{col 14}{res}{space 2} 1.923113{col 26}{space 2} .2388471{col 37}{space 1}    8.05{col 46}{space 3}0.000{col 54}{space 4} 1.454981{col 67}{space 3} 2.391244
{txt}{space 9}30  {c |}{col 14}{res}{space 2} 1.632214{col 26}{space 2}  .260824{col 37}{space 1}    6.26{col 46}{space 3}0.000{col 54}{space 4} 1.121008{col 67}{space 3} 2.143419
{txt}{space 9}31  {c |}{col 14}{res}{space 2} 1.934845{col 26}{space 2} .2465852{col 37}{space 1}    7.85{col 46}{space 3}0.000{col 54}{space 4} 1.451547{col 67}{space 3} 2.418143
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-2.350891{col 26}{space 2} .4130788{col 37}{space 1}   -5.69{col 46}{space 3}0.000{col 54}{space 4} -3.16051{col 67}{space 3}-1.541271
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. predict greenlogit_ideology  if voting==0
{txt}(option {bf:pr} assumed; Pr(green))
(49,494 missing values generated)

{com}. 
. *3rd step: here we predict turnout
. 
. *Compulsory (CEM)
. 
. *this is obtained by summing up : [actual voters (44900 ) + matched abstainers (voting==0  & cem_basic==1)] / (All population)
. 
. *1st column: social
. gen abstain_match_social= cem_social*(11137) if voting==0
{txt}(44,900 missing values generated)

{com}. gen turnout_social = (abstain_match_social+44900 )/56037
{txt}(44,900 missing values generated)

{com}. *2nd column: trust
. gen abstain_match_trust= cem_trust*(11137)  if voting==0
{txt}(44,900 missing values generated)

{com}. gen turnout_trust = (abstain_match_trust+44900 )/56037
{txt}(44,900 missing values generated)

{com}. *3rd column: ideology
. gen abstain_match_ideology = cem_ideology*(11137)  if voting==0
{txt}(44,900 missing values generated)

{com}. gen turnout_ideology= (abstain_match_ideology+44900 )/56037
{txt}(44,900 missing values generated)

{com}. 
. 
. 
. *******************************************************************************************
. **Creating variables for Table 8 in Appendix (Further matching and regression analysis)
. *******************************************************************************************
. 
. 
.  *KERNEL Matching
. 
. 
. quietly: psmatch2 voting  education agea female bebe   minority  y ,    kernel   kerneltype(normal)  odds index logit     common  caliper(0.025)  
{txt}
{com}.   
. gen PSkernel_basic= PS if  _pscore!=.
{txt}(48,787 missing values generated)

{com}. gen leftkernel_basic= left if  _pscore!=.
{txt}(48,787 missing values generated)

{com}. gen greenkernel_basic=green if  _pscore!=.
{txt}(48,787 missing values generated)

{com}. 
. quietly: psmatch2 voting  education agea female bebe   minority    y uemp3m i.source  ,  kernel   kerneltype(normal)  odds index logit     common  caliper(0.025)  
{txt}
{com}.   
. gen PSkernel_augmented= PS if _pscore!=.
{txt}(49,327 missing values generated)

{com}. gen leftkernel_augmented= left if  _pscore!=.
{txt}(49,327 missing values generated)

{com}. gen greenkernel_augmented=green if  _pscore!=.
{txt}(49,327 missing values generated)

{com}. 
. quietly: psmatch2 voting  education agea female bebe   minority  y uemp3m i.source   polintr i.election ,     kernel   kerneltype(normal)  odds index logit     common  caliper(0.025)  
{txt}
{com}.   
. gen PSkernel_full= PS if  _pscore!=.
{txt}(49,264 missing values generated)

{com}. gen leftkernel_full= left if  _pscore!=.
{txt}(49,264 missing values generated)

{com}. gen greenkernel_full=green if  _pscore!=.
{txt}(49,264 missing values generated)

{com}. 
. 
. 
.  *KERNEL Regression
. 
.   npregress  kernel   PS   education agea female bebe minority  y   i.election  

{txt}Computing mean function
{res}  
{txt}{txt:Minimizing cross-validation function:}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 33.236191}  
Iteration 1:{space 3}Cross-validation criterion = {res: 33.236191}  

{p 0 9 2}warning: 44084 observations were not used to compute the mean function because they violated the model identification assumptions. These observations are marked as 1 in the system variable _unident_sample. You may use the {helpb npregress##unidentsample:unidentsample}{bf:()} option to use a different variable name.{p_end}
{res}  
{txt}{txt:Computing optimal derivative bandwidth}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 1.0010582}  
Iteration 1:{space 3}Cross-validation criterion = {res: 1.0009863}  
{res}
{txt}Bandwidth{res}
{txt}{space 0}{hline 13}{c  TT}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Effect}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5282712}}}{space 1}{space 1}{ralign 9:{res:{sf: .6768162}}}{space 1}
{space 0}{space 0}{ralign 12:agea}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  6.54365}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.383663}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1889068}}}{space 1}{space 1}{ralign 9:{res:{sf: .2420256}}}{space 1}
{space 0}{space 0}{ralign 12:bebe}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1826168}}}{space 1}{space 1}{ralign 9:{res:{sf:  .233967}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0590542}}}{space 1}{space 1}{ralign 9:{res:{sf: .0756597}}}{space 1}
{space 0}{space 0}{ralign 12:y}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3135673}}}{space 1}{space 1}{ralign 9:{res:{sf: .4017396}}}{space 1}
{space 0}{space 0}{ralign 12:election}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}

Local-linear regression {col 44}Number of obs      =  {res}        9,403
{txt}Continuous kernel : {result:epanechnikov}{col 43}{help n_npe_note##|_new: E(Kernel obs)}      =  {res}           21
{txt}Discrete kernel   : {result:liracine}{col 44}R-squared          =  {res}       0.1216
{txt}Bandwidth         : {res:cross validation}
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PS{col 14}{c |}   Estimate
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Mean         {txt}{c |}
{space 10}PS {c |}{col 14}{res}{space 2} .2503728
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Effect       {txt}{c |}
{space 3}education {c |}{col 14}{res}{space 2}-.0128388
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0002789
{txt}{space 6}female {c |}{col 14}{res}{space 2}        0
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}        0
{txt}{space 4}minority {c |}{col 14}{res}{space 2}        0
{txt}{space 11}y {c |}{col 14}{res}{space 2}        0
{txt}{space 12} {c |}
{space 4}election {c |}
{space 2} (2 vs 1)  {c |}{col 14}{res}{space 2}-.0076513
{txt}{space 2} (3 vs 1)  {c |}{col 14}{res}{space 2}-.0114619
{txt}{space 2} (4 vs 1)  {c |}{col 14}{res}{space 2} .0067586
{txt}{space 2} (5 vs 1)  {c |}{col 14}{res}{space 2} .0096861
{txt}{space 2} (6 vs 1)  {c |}{col 14}{res}{space 2} -.009716
{txt}{space 2} (7 vs 1)  {c |}{col 14}{res}{space 2}-.0123709
{txt}{space 2} (8 vs 1)  {c |}{col 14}{res}{space 2}-.0084128
{txt}{space 2} (9 vs 1)  {c |}{col 14}{res}{space 2}-.0011582
{txt}{space 2}(10 vs 1)  {c |}{col 14}{res}{space 2}-.0050002
{txt}{space 2}(11 vs 1)  {c |}{col 14}{res}{space 2}-.0069894
{txt}{space 2}(12 vs 1)  {c |}{col 14}{res}{space 2}-.0039399
{txt}{space 2}(13 vs 1)  {c |}{col 14}{res}{space 2}-.0046767
{txt}{space 2}(15 vs 1)  {c |}{col 14}{res}{space 2}-.0076443
{txt}{space 2}(16 vs 1)  {c |}{col 14}{res}{space 2}-.0105024
{txt}{space 2}(17 vs 1)  {c |}{col 14}{res}{space 2}-.0036083
{txt}{space 2}(18 vs 1)  {c |}{col 14}{res}{space 2}-.0088225
{txt}{space 2}(19 vs 1)  {c |}{col 14}{res}{space 2} -.007195
{txt}{space 2}(20 vs 1)  {c |}{col 14}{res}{space 2}-.0100627
{txt}{space 2}(21 vs 1)  {c |}{col 14}{res}{space 2} -.009615
{txt}{space 2}(22 vs 1)  {c |}{col 14}{res}{space 2}-.0144678
{txt}{space 2}(23 vs 1)  {c |}{col 14}{res}{space 2}-.0149481
{txt}{space 2}(24 vs 1)  {c |}{col 14}{res}{space 2}-.0118305
{txt}{space 2}(25 vs 1)  {c |}{col 14}{res}{space 2}-.0071619
{txt}{space 2}(26 vs 1)  {c |}{col 14}{res}{space 2}-.0104111
{txt}{space 2}(27 vs 1)  {c |}{col 14}{res}{space 2}-.0081534
{txt}{space 2}(28 vs 1)  {c |}{col 14}{res}{space 2}-.0183055
{txt}{space 2}(29 vs 1)  {c |}{col 14}{res}{space 2}-.0126906
{txt}{space 2}(30 vs 1)  {c |}{col 14}{res}{space 2}-.0055652
{txt}{space 2}(31 vs 1)  {c |}{col 14}{res}{space 2}-.0049562
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: Effect estimates are averages of derivatives for continuous covariates and averages of contrasts for factor covariates.{p_end}
{p 0 6 2}Note: You may compute standard errors using{txt}{res: vce(bootstrap)} or {res:reps()}.{p_end}

{com}. predict PSkernel_reg_basic 
{txt}(option {bf:mean} assumed; mean function)
(46,634 missing values generated)

{com}.  npregress  kernel   left   education agea female bebe minority  y  i.election 

{txt}Computing mean function
{res}  
{txt}{txt:Minimizing cross-validation function:}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 33.236191}  
Iteration 1:{space 3}Cross-validation criterion = {res: 33.236191}  

{p 0 9 2}warning: 44084 observations were not used to compute the mean function because they violated the model identification assumptions. These observations are marked as 1 in the system variable _unident_sample. You may use the {helpb npregress##unidentsample:unidentsample}{bf:()} option to use a different variable name.{p_end}
{res}  
{txt}{txt:Computing optimal derivative bandwidth}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 1.0004235}  
Iteration 1:{space 3}Cross-validation criterion = {res: 1.0002996}  
Iteration 2:{space 3}Cross-validation criterion = {res: 1.0002996}  
Iteration 3:{space 3}Cross-validation criterion = {res: 1.0002934}  
{res}
{txt}Bandwidth{res}
{txt}{space 0}{hline 13}{c  TT}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Effect}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5282712}}}{space 1}{space 1}{ralign 9:{res:{sf: .6537429}}}{space 1}
{space 0}{space 0}{ralign 12:agea}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  6.54365}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.097857}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1889068}}}{space 1}{space 1}{ralign 9:{res:{sf: .2337748}}}{space 1}
{space 0}{space 0}{ralign 12:bebe}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1826168}}}{space 1}{space 1}{ralign 9:{res:{sf: .2259908}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0590542}}}{space 1}{space 1}{ralign 9:{res:{sf: .0730804}}}{space 1}
{space 0}{space 0}{ralign 12:y}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3135673}}}{space 1}{space 1}{ralign 9:{res:{sf: .3880439}}}{space 1}
{space 0}{space 0}{ralign 12:election}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}

Local-linear regression {col 44}Number of obs      =  {res}        9,403
{txt}Continuous kernel : {result:epanechnikov}{col 43}{help n_npe_note##|_new: E(Kernel obs)}      =  {res}           21
{txt}Discrete kernel   : {result:liracine}{col 44}R-squared          =  {res}       0.1365
{txt}Bandwidth         : {res:cross validation}
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        left{col 14}{c |}   Estimate
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Mean         {txt}{c |}
{space 8}left {c |}{col 14}{res}{space 2} .0575893
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Effect       {txt}{c |}
{space 3}education {c |}{col 14}{res}{space 2} .0096264
{txt}{space 8}agea {c |}{col 14}{res}{space 2} -.000586
{txt}{space 6}female {c |}{col 14}{res}{space 2}        0
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}        0
{txt}{space 4}minority {c |}{col 14}{res}{space 2}        0
{txt}{space 11}y {c |}{col 14}{res}{space 2}        0
{txt}{space 12} {c |}
{space 4}election {c |}
{space 2} (2 vs 1)  {c |}{col 14}{res}{space 2}-.0107405
{txt}{space 2} (3 vs 1)  {c |}{col 14}{res}{space 2}-.0039121
{txt}{space 2} (4 vs 1)  {c |}{col 14}{res}{space 2}-.0125259
{txt}{space 2} (5 vs 1)  {c |}{col 14}{res}{space 2} -.008544
{txt}{space 2} (6 vs 1)  {c |}{col 14}{res}{space 2}-.0072946
{txt}{space 2} (7 vs 1)  {c |}{col 14}{res}{space 2}-.0096797
{txt}{space 2} (8 vs 1)  {c |}{col 14}{res}{space 2}-.0066796
{txt}{space 2} (9 vs 1)  {c |}{col 14}{res}{space 2}-.0082938
{txt}{space 2}(10 vs 1)  {c |}{col 14}{res}{space 2}-.0048762
{txt}{space 2}(11 vs 1)  {c |}{col 14}{res}{space 2}-.0030103
{txt}{space 2}(12 vs 1)  {c |}{col 14}{res}{space 2}-.0035606
{txt}{space 2}(13 vs 1)  {c |}{col 14}{res}{space 2}-.0014991
{txt}{space 2}(15 vs 1)  {c |}{col 14}{res}{space 2} -.003838
{txt}{space 2}(16 vs 1)  {c |}{col 14}{res}{space 2}-.0019309
{txt}{space 2}(17 vs 1)  {c |}{col 14}{res}{space 2}-.0014145
{txt}{space 2}(18 vs 1)  {c |}{col 14}{res}{space 2}-.0019283
{txt}{space 2}(19 vs 1)  {c |}{col 14}{res}{space 2} .0009334
{txt}{space 2}(20 vs 1)  {c |}{col 14}{res}{space 2} .0043182
{txt}{space 2}(21 vs 1)  {c |}{col 14}{res}{space 2} .0009547
{txt}{space 2}(22 vs 1)  {c |}{col 14}{res}{space 2} .0028528
{txt}{space 2}(23 vs 1)  {c |}{col 14}{res}{space 2}-.0000115
{txt}{space 2}(24 vs 1)  {c |}{col 14}{res}{space 2} .0055203
{txt}{space 2}(25 vs 1)  {c |}{col 14}{res}{space 2} .0038913
{txt}{space 2}(26 vs 1)  {c |}{col 14}{res}{space 2} .0046268
{txt}{space 2}(27 vs 1)  {c |}{col 14}{res}{space 2} .0015726
{txt}{space 2}(28 vs 1)  {c |}{col 14}{res}{space 2} .0128719
{txt}{space 2}(29 vs 1)  {c |}{col 14}{res}{space 2} .0173257
{txt}{space 2}(30 vs 1)  {c |}{col 14}{res}{space 2} .0077312
{txt}{space 2}(31 vs 1)  {c |}{col 14}{res}{space 2} .0008961
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: Effect estimates are averages of derivatives for continuous covariates and averages of contrasts for factor covariates.{p_end}
{p 0 6 2}Note: You may compute standard errors using{txt}{res: vce(bootstrap)} or {res:reps()}.{p_end}

{com}. predict leftkernel_reg_basic   
{txt}(option {bf:mean} assumed; mean function)
(46,634 missing values generated)

{com}.   npregress  kernel   green   education agea female bebe minority y  i.election 

{txt}Computing mean function
{res}  
{txt}{txt:Minimizing cross-validation function:}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 33.236191}  
Iteration 1:{space 3}Cross-validation criterion = {res: 33.236191}  

{p 0 9 2}warning: 44084 observations were not used to compute the mean function because they violated the model identification assumptions. These observations are marked as 1 in the system variable _unident_sample. You may use the {helpb npregress##unidentsample:unidentsample}{bf:()} option to use a different variable name.{p_end}
{res}  
{txt}{txt:Computing optimal derivative bandwidth}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 1.0006118}  
Iteration 1:{space 3}Cross-validation criterion = {res: 1.0001161}  
Iteration 2:{space 3}Cross-validation criterion = {res: 1.0001156}  
{res}
{txt}Bandwidth{res}
{txt}{space 0}{hline 13}{c  TT}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Effect}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5282712}}}{space 1}{space 1}{ralign 9:{res:{sf: 43.31624}}}{space 1}
{space 0}{space 0}{ralign 12:agea}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  6.54365}}}{space 1}{space 1}{ralign 9:{res:{sf: 536.5545}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1889068}}}{space 1}{space 1}{ralign 9:{res:{sf: 15.48964}}}{space 1}
{space 0}{space 0}{ralign 12:bebe}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1826168}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.97389}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0590542}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.842219}}}{space 1}
{space 0}{space 0}{ralign 12:y}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3135673}}}{space 1}{space 1}{ralign 9:{res:{sf: 25.71133}}}{space 1}
{space 0}{space 0}{ralign 12:election}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}

Local-linear regression {col 44}Number of obs      =  {res}        9,403
{txt}Continuous kernel : {result:epanechnikov}{col 43}{help n_npe_note##|_new: E(Kernel obs)}      =  {res}           21
{txt}Discrete kernel   : {result:liracine}{col 44}R-squared          =  {res}       0.1291
{txt}Bandwidth         : {res:cross validation}
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       green{col 14}{c |}   Estimate
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Mean         {txt}{c |}
{space 7}green {c |}{col 14}{res}{space 2} .0571205
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Effect       {txt}{c |}
{space 3}education {c |}{col 14}{res}{space 2} .0165947
{txt}{space 8}agea {c |}{col 14}{res}{space 2} -.000526
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0109074
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0013438
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0052071
{txt}{space 11}y {c |}{col 14}{res}{space 2}  .000351
{txt}{space 12} {c |}
{space 4}election {c |}
{space 2} (2 vs 1)  {c |}{col 14}{res}{space 2} .0056304
{txt}{space 2} (3 vs 1)  {c |}{col 14}{res}{space 2} .0104673
{txt}{space 2} (4 vs 1)  {c |}{col 14}{res}{space 2} .0026387
{txt}{space 2} (5 vs 1)  {c |}{col 14}{res}{space 2} .0030181
{txt}{space 2} (6 vs 1)  {c |}{col 14}{res}{space 2} .0099267
{txt}{space 2} (7 vs 1)  {c |}{col 14}{res}{space 2} .0058707
{txt}{space 2} (8 vs 1)  {c |}{col 14}{res}{space 2} .0076518
{txt}{space 2} (9 vs 1)  {c |}{col 14}{res}{space 2} .0109622
{txt}{space 2}(10 vs 1)  {c |}{col 14}{res}{space 2} .0097958
{txt}{space 2}(11 vs 1)  {c |}{col 14}{res}{space 2} .0142797
{txt}{space 2}(12 vs 1)  {c |}{col 14}{res}{space 2} .0097164
{txt}{space 2}(13 vs 1)  {c |}{col 14}{res}{space 2}  .010614
{txt}{space 2}(15 vs 1)  {c |}{col 14}{res}{space 2} .0150539
{txt}{space 2}(16 vs 1)  {c |}{col 14}{res}{space 2} .0133465
{txt}{space 2}(17 vs 1)  {c |}{col 14}{res}{space 2}  .012309
{txt}{space 2}(18 vs 1)  {c |}{col 14}{res}{space 2} .0118828
{txt}{space 2}(19 vs 1)  {c |}{col 14}{res}{space 2} .0133422
{txt}{space 2}(20 vs 1)  {c |}{col 14}{res}{space 2} .0147386
{txt}{space 2}(21 vs 1)  {c |}{col 14}{res}{space 2} .0138847
{txt}{space 2}(22 vs 1)  {c |}{col 14}{res}{space 2}  .017495
{txt}{space 2}(23 vs 1)  {c |}{col 14}{res}{space 2}  .014463
{txt}{space 2}(24 vs 1)  {c |}{col 14}{res}{space 2} .0159651
{txt}{space 2}(25 vs 1)  {c |}{col 14}{res}{space 2} .0157706
{txt}{space 2}(26 vs 1)  {c |}{col 14}{res}{space 2} .0182301
{txt}{space 2}(27 vs 1)  {c |}{col 14}{res}{space 2} .0226897
{txt}{space 2}(28 vs 1)  {c |}{col 14}{res}{space 2} .0214938
{txt}{space 2}(29 vs 1)  {c |}{col 14}{res}{space 2} .0215669
{txt}{space 2}(30 vs 1)  {c |}{col 14}{res}{space 2} .0186194
{txt}{space 2}(31 vs 1)  {c |}{col 14}{res}{space 2} .0215201
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: Effect estimates are averages of derivatives for continuous covariates and averages of contrasts for factor covariates.{p_end}
{p 0 6 2}Note: You may compute standard errors using{txt}{res: vce(bootstrap)} or {res:reps()}.{p_end}

{com}. predict greenkernel_reg_basic  
{txt}(option {bf:mean} assumed; mean function)
(46,634 missing values generated)

{com}. 
.   npregress  kernel   PS   education agea female bebe minority   y uemp3m   i.source    i.election  

{txt}Computing mean function
{res}  
{txt}{txt:Minimizing cross-validation function:}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 33.236191}  
Iteration 1:{space 3}Cross-validation criterion = {res: 33.236191}  

{p 0 9 2}warning: 43576 observations were not used to compute the mean function because they violated the model identification assumptions. These observations are marked as 1 in the system variable _unident_sample. You may use the {helpb npregress##unidentsample:unidentsample}{bf:()} option to use a different variable name.{p_end}
{res}  
{txt}{txt:Computing optimal derivative bandwidth}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 1.0005031}  
Iteration 1:{space 3}Cross-validation criterion = {res: 1.0005031}  
Iteration 2:{space 3}Cross-validation criterion = {res: 1.0005031}  
Iteration 3:{space 3}Cross-validation criterion = {res: 1.0005031}  
Iteration 4:{space 3}Cross-validation criterion = {res: 1.0005031}  
Iteration 5:{space 3}Cross-validation criterion = {res: 1.0005031}  
Iteration 6:{space 3}Cross-validation criterion = {res: 1.0005031}  
Iteration 7:{space 3}Cross-validation criterion = {res: 1.0005031}  
Iteration 8:{space 3}Cross-validation criterion = {res: 1.0005031}  
{res}
{txt}Bandwidth{res}
{txt}{space 0}{hline 13}{c  TT}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Effect}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6143368}}}{space 1}{space 1}{ralign 9:{res:{sf: .6984906}}}{space 1}
{space 0}{space 0}{ralign 12:agea}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 7.593018}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.633134}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .21959}}}{space 1}{space 1}{ralign 9:{res:{sf: .2496701}}}{space 1}
{space 0}{space 0}{ralign 12:bebe}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2123321}}}{space 1}{space 1}{ralign 9:{res:{sf:  .241418}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0687866}}}{space 1}{space 1}{ralign 9:{res:{sf: .0782092}}}{space 1}
{space 0}{space 0}{ralign 12:y}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3639529}}}{space 1}{space 1}{ralign 9:{res:{sf: .4138083}}}{space 1}
{space 0}{space 0}{ralign 12:uemp3m}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1929882}}}{space 1}{space 1}{ralign 9:{res:{sf: .2194243}}}{space 1}
{space 0}{space 0}{ralign 12:source}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{space 0}{ralign 12:election}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}

Local-linear regression {col 44}Number of obs      =  {res}        9,692
{txt}Continuous kernel : {result:epanechnikov}{col 43}{help n_npe_note##|_new: E(Kernel obs)}      =  {res}           10
{txt}Discrete kernel   : {result:liracine}{col 44}R-squared          =  {res}       0.2059
{txt}Bandwidth         : {res:cross validation}
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PS{col 14}{c |}   Estimate
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Mean         {txt}{c |}
{space 10}PS {c |}{col 14}{res}{space 2} .2476866
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Effect       {txt}{c |}
{space 3}education {c |}{col 14}{res}{space 2}-.0163641
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0008039
{txt}{space 6}female {c |}{col 14}{res}{space 2}        0
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}        0
{txt}{space 4}minority {c |}{col 14}{res}{space 2}        0
{txt}{space 11}y {c |}{col 14}{res}{space 2}        0
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}        0
{txt}{space 12} {c |}
{space 6}source {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .0653985
{txt}{space 3}(2 vs 0)  {c |}{col 14}{res}{space 2} .1017761
{txt}{space 3}(3 vs 0)  {c |}{col 14}{res}{space 2} .1383966
{txt}{space 12} {c |}
{space 4}election {c |}
{space 2} (2 vs 1)  {c |}{col 14}{res}{space 2}-.0046303
{txt}{space 2} (3 vs 1)  {c |}{col 14}{res}{space 2}-.0115205
{txt}{space 2} (4 vs 1)  {c |}{col 14}{res}{space 2} .0069429
{txt}{space 2} (5 vs 1)  {c |}{col 14}{res}{space 2} .0088868
{txt}{space 2} (6 vs 1)  {c |}{col 14}{res}{space 2}-.0093905
{txt}{space 2} (7 vs 1)  {c |}{col 14}{res}{space 2}-.0126588
{txt}{space 2} (8 vs 1)  {c |}{col 14}{res}{space 2}-.0075394
{txt}{space 2} (9 vs 1)  {c |}{col 14}{res}{space 2}-.0019817
{txt}{space 2}(10 vs 1)  {c |}{col 14}{res}{space 2}-.0056048
{txt}{space 2}(11 vs 1)  {c |}{col 14}{res}{space 2}-.0084239
{txt}{space 2}(12 vs 1)  {c |}{col 14}{res}{space 2}-.0058473
{txt}{space 2}(13 vs 1)  {c |}{col 14}{res}{space 2}-.0059827
{txt}{space 2}(15 vs 1)  {c |}{col 14}{res}{space 2}-.0102468
{txt}{space 2}(16 vs 1)  {c |}{col 14}{res}{space 2}-.0132313
{txt}{space 2}(17 vs 1)  {c |}{col 14}{res}{space 2}-.0073922
{txt}{space 2}(18 vs 1)  {c |}{col 14}{res}{space 2}-.0134693
{txt}{space 2}(19 vs 1)  {c |}{col 14}{res}{space 2}-.0111013
{txt}{space 2}(20 vs 1)  {c |}{col 14}{res}{space 2}-.0151294
{txt}{space 2}(21 vs 1)  {c |}{col 14}{res}{space 2} -.014658
{txt}{space 2}(22 vs 1)  {c |}{col 14}{res}{space 2}-.0203796
{txt}{space 2}(23 vs 1)  {c |}{col 14}{res}{space 2}-.0200772
{txt}{space 2}(24 vs 1)  {c |}{col 14}{res}{space 2}-.0175726
{txt}{space 2}(25 vs 1)  {c |}{col 14}{res}{space 2}-.0135564
{txt}{space 2}(26 vs 1)  {c |}{col 14}{res}{space 2}-.0170892
{txt}{space 2}(27 vs 1)  {c |}{col 14}{res}{space 2}-.0158926
{txt}{space 2}(28 vs 1)  {c |}{col 14}{res}{space 2}-.0248483
{txt}{space 2}(29 vs 1)  {c |}{col 14}{res}{space 2}-.0187925
{txt}{space 2}(30 vs 1)  {c |}{col 14}{res}{space 2}-.0147882
{txt}{space 2}(31 vs 1)  {c |}{col 14}{res}{space 2}-.0130926
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: Effect estimates are averages of derivatives for continuous covariates and averages of contrasts for factor covariates.{p_end}
{p 0 6 2}Note: You may compute standard errors using{txt}{res: vce(bootstrap)} or {res:reps()}.{p_end}

{com}. predict PSkernel_reg_augmented 
{txt}(option {bf:mean} assumed; mean function)
(46,345 missing values generated)

{com}.   npregress  kernel   left   education agea female bebe minority   y uemp3m   i.source   i.election 

{txt}Computing mean function
{res}  
{txt}{txt:Minimizing cross-validation function:}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 33.236191}  
Iteration 1:{space 3}Cross-validation criterion = {res: 33.236191}  

{p 0 9 2}warning: 43576 observations were not used to compute the mean function because they violated the model identification assumptions. These observations are marked as 1 in the system variable _unident_sample. You may use the {helpb npregress##unidentsample:unidentsample}{bf:()} option to use a different variable name.{p_end}
{res}  
{txt}{txt:Computing optimal derivative bandwidth}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 1.0004838}  
Iteration 1:{space 3}Cross-validation criterion = {res: 1.0004472}  
Iteration 2:{space 3}Cross-validation criterion = {res: 1.0004472}  
Iteration 3:{space 3}Cross-validation criterion = {res: 1.0004472}  
Iteration 4:{space 3}Cross-validation criterion = {res: 1.0004472}  
Iteration 5:{space 3}Cross-validation criterion = {res: 1.0004472}  
Iteration 6:{space 3}Cross-validation criterion = {res: 1.0004472}  
Iteration 7:{space 3}Cross-validation criterion = {res: 1.0004472}  
Iteration 8:{space 3}Cross-validation criterion = {res: 1.0004465}  
{res}
{txt}Bandwidth{res}
{txt}{space 0}{hline 13}{c  TT}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Effect}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6143368}}}{space 1}{space 1}{ralign 9:{res:{sf: .7686125}}}{space 1}
{space 0}{space 0}{ralign 12:agea}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 7.593018}}}{space 1}{space 1}{ralign 9:{res:{sf:  9.49982}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .21959}}}{space 1}{space 1}{ralign 9:{res:{sf: .2747347}}}{space 1}
{space 0}{space 0}{ralign 12:bebe}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2123321}}}{space 1}{space 1}{ralign 9:{res:{sf: .2656541}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0687866}}}{space 1}{space 1}{ralign 9:{res:{sf: .0860606}}}{space 1}
{space 0}{space 0}{ralign 12:y}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3639529}}}{space 1}{space 1}{ralign 9:{res:{sf: .4553507}}}{space 1}
{space 0}{space 0}{ralign 12:uemp3m}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1929882}}}{space 1}{space 1}{ralign 9:{res:{sf: .2414525}}}{space 1}
{space 0}{space 0}{ralign 12:source}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{space 0}{ralign 12:election}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}

Local-linear regression {col 44}Number of obs      =  {res}        9,692
{txt}Continuous kernel : {result:epanechnikov}{col 43}{help n_npe_note##|_new: E(Kernel obs)}      =  {res}           10
{txt}Discrete kernel   : {result:liracine}{col 44}R-squared          =  {res}       0.2306
{txt}Bandwidth         : {res:cross validation}
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        left{col 14}{c |}   Estimate
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Mean         {txt}{c |}
{space 8}left {c |}{col 14}{res}{space 2}  .057725
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Effect       {txt}{c |}
{space 3}education {c |}{col 14}{res}{space 2} .0073687
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0003695
{txt}{space 6}female {c |}{col 14}{res}{space 2}        0
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}        0
{txt}{space 4}minority {c |}{col 14}{res}{space 2}        0
{txt}{space 11}y {c |}{col 14}{res}{space 2} .0146922
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}        0
{txt}{space 12} {c |}
{space 6}source {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .0123847
{txt}{space 3}(2 vs 0)  {c |}{col 14}{res}{space 2}  .018216
{txt}{space 3}(3 vs 0)  {c |}{col 14}{res}{space 2}  .029428
{txt}{space 12} {c |}
{space 4}election {c |}
{space 2} (2 vs 1)  {c |}{col 14}{res}{space 2}-.0086607
{txt}{space 2} (3 vs 1)  {c |}{col 14}{res}{space 2}-.0012675
{txt}{space 2} (4 vs 1)  {c |}{col 14}{res}{space 2}-.0101221
{txt}{space 2} (5 vs 1)  {c |}{col 14}{res}{space 2}-.0062528
{txt}{space 2} (6 vs 1)  {c |}{col 14}{res}{space 2}-.0050885
{txt}{space 2} (7 vs 1)  {c |}{col 14}{res}{space 2}-.0064876
{txt}{space 2} (8 vs 1)  {c |}{col 14}{res}{space 2}-.0035039
{txt}{space 2} (9 vs 1)  {c |}{col 14}{res}{space 2}-.0049329
{txt}{space 2}(10 vs 1)  {c |}{col 14}{res}{space 2}  -.00151
{txt}{space 2}(11 vs 1)  {c |}{col 14}{res}{space 2} .0001069
{txt}{space 2}(12 vs 1)  {c |}{col 14}{res}{space 2} .0000856
{txt}{space 2}(13 vs 1)  {c |}{col 14}{res}{space 2} .0022671
{txt}{space 2}(15 vs 1)  {c |}{col 14}{res}{space 2} .0003458
{txt}{space 2}(16 vs 1)  {c |}{col 14}{res}{space 2} .0023067
{txt}{space 2}(17 vs 1)  {c |}{col 14}{res}{space 2} .0030194
{txt}{space 2}(18 vs 1)  {c |}{col 14}{res}{space 2} .0028232
{txt}{space 2}(19 vs 1)  {c |}{col 14}{res}{space 2} .0056478
{txt}{space 2}(20 vs 1)  {c |}{col 14}{res}{space 2} .0089127
{txt}{space 2}(21 vs 1)  {c |}{col 14}{res}{space 2} .0060222
{txt}{space 2}(22 vs 1)  {c |}{col 14}{res}{space 2} .0079407
{txt}{space 2}(23 vs 1)  {c |}{col 14}{res}{space 2} .0056092
{txt}{space 2}(24 vs 1)  {c |}{col 14}{res}{space 2} .0111503
{txt}{space 2}(25 vs 1)  {c |}{col 14}{res}{space 2} .0095093
{txt}{space 2}(26 vs 1)  {c |}{col 14}{res}{space 2} .0104742
{txt}{space 2}(27 vs 1)  {c |}{col 14}{res}{space 2} .0076636
{txt}{space 2}(28 vs 1)  {c |}{col 14}{res}{space 2} .0198612
{txt}{space 2}(29 vs 1)  {c |}{col 14}{res}{space 2} .0220411
{txt}{space 2}(30 vs 1)  {c |}{col 14}{res}{space 2} .0142518
{txt}{space 2}(31 vs 1)  {c |}{col 14}{res}{space 2} .0085436
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: Effect estimates are averages of derivatives for continuous covariates and averages of contrasts for factor covariates.{p_end}
{p 0 6 2}Note: You may compute standard errors using{txt}{res: vce(bootstrap)} or {res:reps()}.{p_end}

{com}. predict leftkernel_reg_augmented  
{txt}(option {bf:mean} assumed; mean function)
(46,345 missing values generated)

{com}.  npregress  kernel   green   education agea female bebe minority   y uemp3m   i.source   i.election 

{txt}Computing mean function
{res}  
{txt}{txt:Minimizing cross-validation function:}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 33.236191}  
Iteration 1:{space 3}Cross-validation criterion = {res: 33.236191}  

{p 0 9 2}warning: 43576 observations were not used to compute the mean function because they violated the model identification assumptions. These observations are marked as 1 in the system variable _unident_sample. You may use the {helpb npregress##unidentsample:unidentsample}{bf:()} option to use a different variable name.{p_end}
{res}  
{txt}{txt:Computing optimal derivative bandwidth}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 1.0005161}  
Iteration 1:{space 3}Cross-validation criterion = {res: 1.0004685}  
Iteration 2:{space 3}Cross-validation criterion = {res: 1.0004685}  
Iteration 3:{space 3}Cross-validation criterion = {res: 1.0004685}  
Iteration 4:{space 3}Cross-validation criterion = {res: 1.0004685}  
Iteration 5:{space 3}Cross-validation criterion = {res: 1.0004685}  
Iteration 6:{space 3}Cross-validation criterion = {res: 1.0001988}  
Iteration 7:{space 3}Cross-validation criterion = {res: 1.0001986}  
{res}
{txt}Bandwidth{res}
{txt}{space 0}{hline 13}{c  TT}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Effect}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6143368}}}{space 1}{space 1}{ralign 9:{res:{sf: 32.98622}}}{space 1}
{space 0}{space 0}{ralign 12:agea}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 7.593018}}}{space 1}{space 1}{ralign 9:{res:{sf: 407.6998}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .21959}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.79067}}}{space 1}
{space 0}{space 0}{ralign 12:bebe}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2123321}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.40097}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0687866}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.693427}}}{space 1}
{space 0}{space 0}{ralign 12:y}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3639529}}}{space 1}{space 1}{ralign 9:{res:{sf:  19.5421}}}{space 1}
{space 0}{space 0}{ralign 12:uemp3m}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1929882}}}{space 1}{space 1}{ralign 9:{res:{sf: 10.36231}}}{space 1}
{space 0}{space 0}{ralign 12:source}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{space 0}{ralign 12:election}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}

Local-linear regression {col 44}Number of obs      =  {res}        9,692
{txt}Continuous kernel : {result:epanechnikov}{col 43}{help n_npe_note##|_new: E(Kernel obs)}      =  {res}           10
{txt}Discrete kernel   : {result:liracine}{col 44}R-squared          =  {res}       0.2263
{txt}Bandwidth         : {res:cross validation}
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       green{col 14}{c |}   Estimate
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Mean         {txt}{c |}
{space 7}green {c |}{col 14}{res}{space 2} .0568347
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Effect       {txt}{c |}
{space 3}education {c |}{col 14}{res}{space 2} .0159362
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0004499
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0114709
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0014367
{txt}{space 4}minority {c |}{col 14}{res}{space 2}-.0002139
{txt}{space 11}y {c |}{col 14}{res}{space 2}-.0022715
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.0149133
{txt}{space 12} {c |}
{space 6}source {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.0045955
{txt}{space 3}(2 vs 0)  {c |}{col 14}{res}{space 2}-.0062029
{txt}{space 3}(3 vs 0)  {c |}{col 14}{res}{space 2}-.0150538
{txt}{space 12} {c |}
{space 4}election {c |}
{space 2} (2 vs 1)  {c |}{col 14}{res}{space 2} .0046953
{txt}{space 2} (3 vs 1)  {c |}{col 14}{res}{space 2} .0085459
{txt}{space 2} (4 vs 1)  {c |}{col 14}{res}{space 2} .0008134
{txt}{space 2} (5 vs 1)  {c |}{col 14}{res}{space 2} .0012706
{txt}{space 2} (6 vs 1)  {c |}{col 14}{res}{space 2} .0073801
{txt}{space 2} (7 vs 1)  {c |}{col 14}{res}{space 2} .0032564
{txt}{space 2} (8 vs 1)  {c |}{col 14}{res}{space 2} .0047852
{txt}{space 2} (9 vs 1)  {c |}{col 14}{res}{space 2} .0072651
{txt}{space 2}(10 vs 1)  {c |}{col 14}{res}{space 2} .0060975
{txt}{space 2}(11 vs 1)  {c |}{col 14}{res}{space 2} .0102591
{txt}{space 2}(12 vs 1)  {c |}{col 14}{res}{space 2} .0053958
{txt}{space 2}(13 vs 1)  {c |}{col 14}{res}{space 2} .0059388
{txt}{space 2}(15 vs 1)  {c |}{col 14}{res}{space 2} .0095552
{txt}{space 2}(16 vs 1)  {c |}{col 14}{res}{space 2} .0076625
{txt}{space 2}(17 vs 1)  {c |}{col 14}{res}{space 2} .0063909
{txt}{space 2}(18 vs 1)  {c |}{col 14}{res}{space 2} .0058704
{txt}{space 2}(19 vs 1)  {c |}{col 14}{res}{space 2} .0071513
{txt}{space 2}(20 vs 1)  {c |}{col 14}{res}{space 2} .0075004
{txt}{space 2}(21 vs 1)  {c |}{col 14}{res}{space 2} .0065253
{txt}{space 2}(22 vs 1)  {c |}{col 14}{res}{space 2} .0095742
{txt}{space 2}(23 vs 1)  {c |}{col 14}{res}{space 2} .0067104
{txt}{space 2}(24 vs 1)  {c |}{col 14}{res}{space 2} .0074354
{txt}{space 2}(25 vs 1)  {c |}{col 14}{res}{space 2}  .007259
{txt}{space 2}(26 vs 1)  {c |}{col 14}{res}{space 2} .0093445
{txt}{space 2}(27 vs 1)  {c |}{col 14}{res}{space 2} .0131888
{txt}{space 2}(28 vs 1)  {c |}{col 14}{res}{space 2} .0118293
{txt}{space 2}(29 vs 1)  {c |}{col 14}{res}{space 2} .0108345
{txt}{space 2}(30 vs 1)  {c |}{col 14}{res}{space 2} .0082904
{txt}{space 2}(31 vs 1)  {c |}{col 14}{res}{space 2} .0103738
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: Effect estimates are averages of derivatives for continuous covariates and averages of contrasts for factor covariates.{p_end}
{p 0 6 2}Note: You may compute standard errors using{txt}{res: vce(bootstrap)} or {res:reps()}.{p_end}

{com}. predict greenkernel_reg_augmented  
{txt}(option {bf:mean} assumed; mean function)
(46,345 missing values generated)

{com}. 
.   npregress  kernel   PS   education agea female bebe minority   y uemp3m  i.source polintr     i.election  

{txt}Computing mean function
{res}  
{txt}{txt:Minimizing cross-validation function:}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 33.236191}  
Iteration 1:{space 3}Cross-validation criterion = {res: 33.236191}  

{p 0 9 2}warning: 43480 observations were not used to compute the mean function because they violated the model identification assumptions. These observations are marked as 1 in the system variable _unident_sample. You may use the {helpb npregress##unidentsample:unidentsample}{bf:()} option to use a different variable name.{p_end}
{res}  
{txt}{txt:Computing optimal derivative bandwidth}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 1.0007506}  
Iteration 1:{space 3}Cross-validation criterion = {res: 1.0007506}  
{res}
{txt}Bandwidth{res}
{txt}{space 0}{hline 13}{c  TT}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Effect}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .651847}}}{space 1}{space 1}{ralign 9:{res:{sf:  .727162}}}{space 1}
{space 0}{space 0}{ralign 12:agea}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 8.051591}}}{space 1}{space 1}{ralign 9:{res:{sf:  8.98188}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2328973}}}{space 1}{space 1}{ralign 9:{res:{sf: .2598064}}}{space 1}
{space 0}{space 0}{ralign 12:bebe}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2252048}}}{space 1}{space 1}{ralign 9:{res:{sf: .2512251}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0730021}}}{space 1}{space 1}{ralign 9:{res:{sf: .0814369}}}{space 1}
{space 0}{space 0}{ralign 12:y}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3860427}}}{space 1}{space 1}{ralign 9:{res:{sf: .4306464}}}{space 1}
{space 0}{space 0}{ralign 12:uemp3m}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2047335}}}{space 1}{space 1}{ralign 9:{res:{sf: .2283886}}}{space 1}
{space 0}{space 0}{ralign 12:source}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{space 0}{ralign 12:polintr}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4225811}}}{space 1}{space 1}{ralign 9:{res:{sf: .4714065}}}{space 1}
{space 0}{space 0}{ralign 12:election}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}

Local-linear regression {col 44}Number of obs      =  {res}        9,745
{txt}Continuous kernel : {result:epanechnikov}{col 43}{help n_npe_note##|_new: E(Kernel obs)}      =  {res}            7
{txt}Discrete kernel   : {result:liracine}{col 44}R-squared          =  {res}       0.3728
{txt}Bandwidth         : {res:cross validation}
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PS{col 14}{c |}   Estimate
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Mean         {txt}{c |}
{space 10}PS {c |}{col 14}{res}{space 2} .2464413
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Effect       {txt}{c |}
{space 3}education {c |}{col 14}{res}{space 2}-.0175508
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0006834
{txt}{space 6}female {c |}{col 14}{res}{space 2}        0
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}        0
{txt}{space 4}minority {c |}{col 14}{res}{space 2}        0
{txt}{space 11}y {c |}{col 14}{res}{space 2}        0
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}        0
{txt}{space 5}polintr {c |}{col 14}{res}{space 2} -.007476
{txt}{space 12} {c |}
{space 6}source {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .0602685
{txt}{space 3}(2 vs 0)  {c |}{col 14}{res}{space 2} .0979619
{txt}{space 3}(3 vs 0)  {c |}{col 14}{res}{space 2} .1314171
{txt}{space 12} {c |}
{space 4}election {c |}
{space 2} (2 vs 1)  {c |}{col 14}{res}{space 2}-.0045414
{txt}{space 2} (3 vs 1)  {c |}{col 14}{res}{space 2}-.0095433
{txt}{space 2} (4 vs 1)  {c |}{col 14}{res}{space 2} .0048807
{txt}{space 2} (5 vs 1)  {c |}{col 14}{res}{space 2} .0058688
{txt}{space 2} (6 vs 1)  {c |}{col 14}{res}{space 2}-.0085013
{txt}{space 2} (7 vs 1)  {c |}{col 14}{res}{space 2}-.0115703
{txt}{space 2} (8 vs 1)  {c |}{col 14}{res}{space 2}-.0067065
{txt}{space 2} (9 vs 1)  {c |}{col 14}{res}{space 2}-.0015962
{txt}{space 2}(10 vs 1)  {c |}{col 14}{res}{space 2}-.0052554
{txt}{space 2}(11 vs 1)  {c |}{col 14}{res}{space 2}-.0068419
{txt}{space 2}(12 vs 1)  {c |}{col 14}{res}{space 2}-.0047067
{txt}{space 2}(13 vs 1)  {c |}{col 14}{res}{space 2}-.0040477
{txt}{space 2}(15 vs 1)  {c |}{col 14}{res}{space 2} -.008282
{txt}{space 2}(16 vs 1)  {c |}{col 14}{res}{space 2}-.0110327
{txt}{space 2}(17 vs 1)  {c |}{col 14}{res}{space 2}-.0049168
{txt}{space 2}(18 vs 1)  {c |}{col 14}{res}{space 2}-.0099655
{txt}{space 2}(19 vs 1)  {c |}{col 14}{res}{space 2}-.0077492
{txt}{space 2}(20 vs 1)  {c |}{col 14}{res}{space 2}-.0111896
{txt}{space 2}(21 vs 1)  {c |}{col 14}{res}{space 2}-.0107112
{txt}{space 2}(22 vs 1)  {c |}{col 14}{res}{space 2}-.0157224
{txt}{space 2}(23 vs 1)  {c |}{col 14}{res}{space 2}-.0156615
{txt}{space 2}(24 vs 1)  {c |}{col 14}{res}{space 2}-.0129284
{txt}{space 2}(25 vs 1)  {c |}{col 14}{res}{space 2}-.0100913
{txt}{space 2}(26 vs 1)  {c |}{col 14}{res}{space 2}-.0124906
{txt}{space 2}(27 vs 1)  {c |}{col 14}{res}{space 2}-.0120845
{txt}{space 2}(28 vs 1)  {c |}{col 14}{res}{space 2}-.0182074
{txt}{space 2}(29 vs 1)  {c |}{col 14}{res}{space 2} -.014724
{txt}{space 2}(30 vs 1)  {c |}{col 14}{res}{space 2}-.0093925
{txt}{space 2}(31 vs 1)  {c |}{col 14}{res}{space 2}-.0094379
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: Effect estimates are averages of derivatives for continuous covariates and averages of contrasts for factor covariates.{p_end}
{p 0 6 2}Note: You may compute standard errors using{txt}{res: vce(bootstrap)} or {res:reps()}.{p_end}

{com}. predict PSkernel_reg_full 
{txt}(option {bf:mean} assumed; mean function)
(46,292 missing values generated)

{com}.   npregress  kernel   left   education agea female bebe minority   y uemp3m  i.source polintr     i.election 

{txt}Computing mean function
{res}  
{txt}{txt:Minimizing cross-validation function:}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 33.236191}  
Iteration 1:{space 3}Cross-validation criterion = {res: 33.236191}  

{p 0 9 2}warning: 43480 observations were not used to compute the mean function because they violated the model identification assumptions. These observations are marked as 1 in the system variable _unident_sample. You may use the {helpb npregress##unidentsample:unidentsample}{bf:()} option to use a different variable name.{p_end}
{res}  
{txt}{txt:Computing optimal derivative bandwidth}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 1.0005815}  
Iteration 1:{space 3}Cross-validation criterion = {res: 1.0003138}  
Iteration 2:{space 3}Cross-validation criterion = {res: 1.0003138}  
{res}
{txt}Bandwidth{res}
{txt}{space 0}{hline 13}{c  TT}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Effect}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .651847}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.635505}}}{space 1}
{space 0}{space 0}{ralign 12:agea}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 8.051591}}}{space 1}{space 1}{ralign 9:{res:{sf: 69.60957}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2328973}}}{space 1}{space 1}{ralign 9:{res:{sf:   2.0135}}}{space 1}
{space 0}{space 0}{ralign 12:bebe}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2252048}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.946995}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0730021}}}{space 1}{space 1}{ralign 9:{res:{sf: .6311356}}}{space 1}
{space 0}{space 0}{ralign 12:y}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3860427}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.337509}}}{space 1}
{space 0}{space 0}{ralign 12:uemp3m}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2047335}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.770012}}}{space 1}
{space 0}{space 0}{ralign 12:source}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{space 0}{ralign 12:polintr}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4225811}}}{space 1}{space 1}{ralign 9:{res:{sf:   3.6534}}}{space 1}
{space 0}{space 0}{ralign 12:election}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}

Local-linear regression {col 44}Number of obs      =  {res}        9,745
{txt}Continuous kernel : {result:epanechnikov}{col 43}{help n_npe_note##|_new: E(Kernel obs)}      =  {res}            7
{txt}Discrete kernel   : {result:liracine}{col 44}R-squared          =  {res}       0.4120
{txt}Bandwidth         : {res:cross validation}
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        left{col 14}{c |}   Estimate
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Mean         {txt}{c |}
{space 8}left {c |}{col 14}{res}{space 2} .0571946
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Effect       {txt}{c |}
{space 3}education {c |}{col 14}{res}{space 2}  .005895
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0004987
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0043106
{txt}{space 8}bebe {c |}{col 14}{res}{space 2} .0040429
{txt}{space 4}minority {c |}{col 14}{res}{space 2} .0124622
{txt}{space 11}y {c |}{col 14}{res}{space 2} .0142774
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}-.0268411
{txt}{space 5}polintr {c |}{col 14}{res}{space 2}-.0098508
{txt}{space 12} {c |}
{space 6}source {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .0090241
{txt}{space 3}(2 vs 0)  {c |}{col 14}{res}{space 2} .0142103
{txt}{space 3}(3 vs 0)  {c |}{col 14}{res}{space 2} .0195726
{txt}{space 12} {c |}
{space 4}election {c |}
{space 2} (2 vs 1)  {c |}{col 14}{res}{space 2}-.0077225
{txt}{space 2} (3 vs 1)  {c |}{col 14}{res}{space 2}-.0024445
{txt}{space 2} (4 vs 1)  {c |}{col 14}{res}{space 2}-.0092304
{txt}{space 2} (5 vs 1)  {c |}{col 14}{res}{space 2}-.0065354
{txt}{space 2} (6 vs 1)  {c |}{col 14}{res}{space 2}-.0051312
{txt}{space 2} (7 vs 1)  {c |}{col 14}{res}{space 2}-.0073733
{txt}{space 2} (8 vs 1)  {c |}{col 14}{res}{space 2}-.0049878
{txt}{space 2} (9 vs 1)  {c |}{col 14}{res}{space 2}-.0062931
{txt}{space 2}(10 vs 1)  {c |}{col 14}{res}{space 2}-.0035283
{txt}{space 2}(11 vs 1)  {c |}{col 14}{res}{space 2}-.0028145
{txt}{space 2}(12 vs 1)  {c |}{col 14}{res}{space 2}-.0025904
{txt}{space 2}(13 vs 1)  {c |}{col 14}{res}{space 2}-.0009396
{txt}{space 2}(15 vs 1)  {c |}{col 14}{res}{space 2} -.003116
{txt}{space 2}(16 vs 1)  {c |}{col 14}{res}{space 2}-.0017847
{txt}{space 2}(17 vs 1)  {c |}{col 14}{res}{space 2}-.0015548
{txt}{space 2}(18 vs 1)  {c |}{col 14}{res}{space 2}-.0015824
{txt}{space 2}(19 vs 1)  {c |}{col 14}{res}{space 2} .0002433
{txt}{space 2}(20 vs 1)  {c |}{col 14}{res}{space 2} .0026996
{txt}{space 2}(21 vs 1)  {c |}{col 14}{res}{space 2} .0002402
{txt}{space 2}(22 vs 1)  {c |}{col 14}{res}{space 2} .0015747
{txt}{space 2}(23 vs 1)  {c |}{col 14}{res}{space 2}-.0003116
{txt}{space 2}(24 vs 1)  {c |}{col 14}{res}{space 2} .0032843
{txt}{space 2}(25 vs 1)  {c |}{col 14}{res}{space 2} .0022189
{txt}{space 2}(26 vs 1)  {c |}{col 14}{res}{space 2} .0027929
{txt}{space 2}(27 vs 1)  {c |}{col 14}{res}{space 2} .0001693
{txt}{space 2}(28 vs 1)  {c |}{col 14}{res}{space 2} .0099918
{txt}{space 2}(29 vs 1)  {c |}{col 14}{res}{space 2} .0110377
{txt}{space 2}(30 vs 1)  {c |}{col 14}{res}{space 2} .0045981
{txt}{space 2}(31 vs 1)  {c |}{col 14}{res}{space 2}-.0001484
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: Effect estimates are averages of derivatives for continuous covariates and averages of contrasts for factor covariates.{p_end}
{p 0 6 2}Note: You may compute standard errors using{txt}{res: vce(bootstrap)} or {res:reps()}.{p_end}

{com}. predict leftkernel_reg_full 
{txt}(option {bf:mean} assumed; mean function)
(46,292 missing values generated)

{com}.   npregress  kernel   green   education agea female bebe minority   y uemp3m  i.source polintr     i.election 

{txt}Computing mean function
{res}  
{txt}{txt:Minimizing cross-validation function:}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 33.236191}  
Iteration 1:{space 3}Cross-validation criterion = {res: 33.236191}  

{p 0 9 2}warning: 43480 observations were not used to compute the mean function because they violated the model identification assumptions. These observations are marked as 1 in the system variable _unident_sample. You may use the {helpb npregress##unidentsample:unidentsample}{bf:()} option to use a different variable name.{p_end}
{res}  
{txt}{txt:Computing optimal derivative bandwidth}
  
Iteration 0:{space 3}Cross-validation criterion = {res: 1.0005381}  
Iteration 1:{space 3}Cross-validation criterion = {res: 1.0005381}  
Iteration 2:{space 3}Cross-validation criterion = {res: 1.0005381}  
Iteration 3:{space 3}Cross-validation criterion = {res: 1.0005381}  
{res}
{txt}Bandwidth{res}
{txt}{space 0}{hline 13}{c  TT}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Effect}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .651847}}}{space 1}{space 1}{ralign 9:{res:{sf:  .727162}}}{space 1}
{space 0}{space 0}{ralign 12:agea}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 8.051591}}}{space 1}{space 1}{ralign 9:{res:{sf:  8.98188}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2328973}}}{space 1}{space 1}{ralign 9:{res:{sf: .2598064}}}{space 1}
{space 0}{space 0}{ralign 12:bebe}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2252048}}}{space 1}{space 1}{ralign 9:{res:{sf: .2512251}}}{space 1}
{space 0}{space 0}{ralign 12:minority}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0730021}}}{space 1}{space 1}{ralign 9:{res:{sf: .0814369}}}{space 1}
{space 0}{space 0}{ralign 12:y}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3860427}}}{space 1}{space 1}{ralign 9:{res:{sf: .4306464}}}{space 1}
{space 0}{space 0}{ralign 12:uemp3m}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2047335}}}{space 1}{space 1}{ralign 9:{res:{sf: .2283886}}}{space 1}
{space 0}{space 0}{ralign 12:source}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{space 0}{ralign 12:polintr}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4225811}}}{space 1}{space 1}{ralign 9:{res:{sf: .4714065}}}{space 1}
{space 0}{space 0}{ralign 12:election}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}{space 1}{ralign 9:{res:{sf:       .5}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}

Local-linear regression {col 44}Number of obs      =  {res}        9,745
{txt}Continuous kernel : {result:epanechnikov}{col 43}{help n_npe_note##|_new: E(Kernel obs)}      =  {res}            7
{txt}Discrete kernel   : {result:liracine}{col 44}R-squared          =  {res}       0.4091
{txt}Bandwidth         : {res:cross validation}
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       green{col 14}{c |}   Estimate
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Mean         {txt}{c |}
{space 7}green {c |}{col 14}{res}{space 2} .0563865
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Effect       {txt}{c |}
{space 3}education {c |}{col 14}{res}{space 2} .0162683
{txt}{space 8}agea {c |}{col 14}{res}{space 2}-.0003573
{txt}{space 6}female {c |}{col 14}{res}{space 2}        0
{txt}{space 8}bebe {c |}{col 14}{res}{space 2}        0
{txt}{space 4}minority {c |}{col 14}{res}{space 2}        0
{txt}{space 11}y {c |}{col 14}{res}{space 2}        0
{txt}{space 6}uemp3m {c |}{col 14}{res}{space 2}        0
{txt}{space 5}polintr {c |}{col 14}{res}{space 2}-.0143077
{txt}{space 12} {c |}
{space 6}source {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.0061685
{txt}{space 3}(2 vs 0)  {c |}{col 14}{res}{space 2}-.0130253
{txt}{space 3}(3 vs 0)  {c |}{col 14}{res}{space 2}-.0223216
{txt}{space 12} {c |}
{space 4}election {c |}
{space 2} (2 vs 1)  {c |}{col 14}{res}{space 2}  .004018
{txt}{space 2} (3 vs 1)  {c |}{col 14}{res}{space 2} .0069987
{txt}{space 2} (4 vs 1)  {c |}{col 14}{res}{space 2} .0002777
{txt}{space 2} (5 vs 1)  {c |}{col 14}{res}{space 2} .0006228
{txt}{space 2} (6 vs 1)  {c |}{col 14}{res}{space 2}  .005072
{txt}{space 2} (7 vs 1)  {c |}{col 14}{res}{space 2} .0014964
{txt}{space 2} (8 vs 1)  {c |}{col 14}{res}{space 2} .0027933
{txt}{space 2} (9 vs 1)  {c |}{col 14}{res}{space 2} .0037973
{txt}{space 2}(10 vs 1)  {c |}{col 14}{res}{space 2} .0033982
{txt}{space 2}(11 vs 1)  {c |}{col 14}{res}{space 2} .0063189
{txt}{space 2}(12 vs 1)  {c |}{col 14}{res}{space 2} .0021432
{txt}{space 2}(13 vs 1)  {c |}{col 14}{res}{space 2} .0025577
{txt}{space 2}(15 vs 1)  {c |}{col 14}{res}{space 2} .0047394
{txt}{space 2}(16 vs 1)  {c |}{col 14}{res}{space 2} .0029995
{txt}{space 2}(17 vs 1)  {c |}{col 14}{res}{space 2}  .001226
{txt}{space 2}(18 vs 1)  {c |}{col 14}{res}{space 2} .0006883
{txt}{space 2}(19 vs 1)  {c |}{col 14}{res}{space 2} .0013812
{txt}{space 2}(20 vs 1)  {c |}{col 14}{res}{space 2} .0012327
{txt}{space 2}(21 vs 1)  {c |}{col 14}{res}{space 2} .0005408
{txt}{space 2}(22 vs 1)  {c |}{col 14}{res}{space 2} .0026033
{txt}{space 2}(23 vs 1)  {c |}{col 14}{res}{space 2} .0002884
{txt}{space 2}(24 vs 1)  {c |}{col 14}{res}{space 2} .0000339
{txt}{space 2}(25 vs 1)  {c |}{col 14}{res}{space 2}-.0002712
{txt}{space 2}(26 vs 1)  {c |}{col 14}{res}{space 2} .0015162
{txt}{space 2}(27 vs 1)  {c |}{col 14}{res}{space 2} .0042074
{txt}{space 2}(28 vs 1)  {c |}{col 14}{res}{space 2} .0031983
{txt}{space 2}(29 vs 1)  {c |}{col 14}{res}{space 2}  .001542
{txt}{space 2}(30 vs 1)  {c |}{col 14}{res}{space 2}-.0013181
{txt}{space 2}(31 vs 1)  {c |}{col 14}{res}{space 2} .0009202
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: Effect estimates are averages of derivatives for continuous covariates and averages of contrasts for factor covariates.{p_end}
{p 0 6 2}Note: You may compute standard errors using{txt}{res: vce(bootstrap)} or {res:reps()}.{p_end}

{com}. predict greenkernel_reg_full 
{txt}(option {bf:mean} assumed; mean function)
(46,292 missing values generated)

{com}. 
. save data.dta, replace
{txt}file data.dta saved

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\k1801607\Dropbox\New_politicization\RR_PSRM_replica\session.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}30 Jun 2019, 10:27:51
{txt}{.-}
{smcl}
{txt}{sf}{ul off}